#Variables in Statistical Analysis

[juliebowie]

Understanding Different Types of Variables in Statistical Analysis

Summary: This blog delves into the types of variables in statistical analysis, including quantitative (continuous and discrete) and qualitative (nominal and ordinal). Understanding these variables is critical for practical data interpretation and statistical analysis.

Introduction

Statistical analysis is crucial in research and data interpretation, providing insights that guide decision-making and uncover trends. By analysing data systematically, researchers can draw meaningful conclusions and validate hypotheses. 

Understanding the types of variables in statistical analysis is essential for accurate data interpretation. Variables representing different data aspects play a crucial role in shaping statistical results. 

This blog aims to explore the various types of variables in statistical analysis, explaining their definitions and applications to enhance your grasp of how they influence data analysis and research outcomes.

What is Statistical Analysis?

Statistical analysis involves applying mathematical techniques to understand, interpret, and summarise data. It transforms raw data into meaningful insights by identifying patterns, trends, and relationships. The primary purpose is to make informed decisions based on data, whether for academic research, business strategy, or policy-making.

How Statistical Analysis Helps in Drawing Conclusions

Statistical analysis aids in concluding by providing a structured approach to data examination. It involves summarising data through measures of central tendency (mean, median, mode) and variability (range, variance, standard deviation). By using these summaries, analysts can detect trends and anomalies. 

More advanced techniques, such as hypothesis testing and regression analysis, help make predictions and determine the relationships between variables. These insights allow decision-makers to base their actions on empirical evidence rather than intuition.

Types of Statistical Analyses

Analysts can effectively interpret data, support their findings with evidence, and make well-informed decisions by employing both descriptive and inferential statistics.

Descriptive Statistics: This type focuses on summarising and describing the features of a dataset. Techniques include calculating averages and percentages and crating visual representations like charts and graphs. Descriptive statistics provide a snapshot of the data, making it easier to understand and communicate.

Inferential Statistics: Inferential analysis goes beyond summarisation to make predictions or generalisations about a population based on a sample. It includes hypothesis testing, confidence intervals, and regression analysis. This type of analysis helps conclude a broader context from the data collected from a smaller subset.

What are Variables in Statistical Analysis?

In statistical analysis, a variable represents a characteristic or attribute that can take on different values. Variables are the foundation for collecting and analysing data, allowing researchers to quantify and examine various study aspects. They are essential components in research, as they help identify patterns, relationships, and trends within the data.

How Variables Represent Data

Variables act as placeholders for data points and can be used to measure different aspects of a study. For instance, variables might include test scores, study hours, and socioeconomic status in a survey of student performance. 

Researchers can systematically analyse how different factors influence outcomes by assigning numerical or categorical values to these variables. This process involves collecting data, organising it, and then applying statistical techniques to draw meaningful conclusions.

Importance of Understanding Variables

Understanding variables is crucial for accurate data analysis and interpretation. Continuous, discrete, nominal, and ordinal variables affect how data is analysed and interpreted. For example, continuous variables like height or weight can be measured precisely. In contrast, nominal variables like gender or ethnicity categorise data without implying order. 

Researchers can apply appropriate statistical methods and avoid misleading results by correctly identifying and using variables. Accurate analysis hinges on a clear grasp of variable types and their roles in the research process, interpreting data more reliable and actionable.

Types of Variables in Statistical Analysis

Understanding the different types of variables in statistical analysis is crucial for practical data interpretation and decision-making. Variables are characteristics or attributes that researchers measure and analyse to uncover patterns, relationships, and insights. These variables can be broadly categorised into quantitative and qualitative types, each with distinct characteristics and significance.

Quantitative Variables

Quantitative variables represent measurable quantities and can be expressed numerically. They allow researchers to perform mathematical operations and statistical analyses to derive insights.

Continuous Variables

Continuous variables can take on infinite values within a given range. These variables can be measured precisely, and their values are not limited to specific discrete points.

Examples of continuous variables include height, weight, temperature, and time. For instance, a person's height can be measured with varying degrees of precision, from centimetres to millimetres, and it can fall anywhere within a specific range.

Continuous variables are crucial for analyses that require detailed and precise measurement. They enable researchers to conduct a wide range of statistical tests, such as calculating averages and standard deviations and performing regression analyses. The granularity of continuous variables allows for nuanced insights and more accurate predictions.

Discrete Variables

Discrete variables can only take on separate values. Unlike continuous variables, discrete variables cannot be subdivided into finer increments and are often counted rather than measured.

Examples of discrete variables include the number of students in a class, the number of cars in a parking lot, and the number of errors in a software application. For instance, you can count 15 students in a class, but you cannot have 15.5 students.

Discrete variables are essential when counting or categorising is required. They are often used in frequency distributions and categorical data analysis. Statistical methods for discrete variables include chi-square tests and Poisson regression, which are valuable for analysing count-based data and understanding categorical outcomes.

Qualitative Variables

Qualitative or categorical variables describe characteristics or attributes that cannot be measured numerically but can be classified into categories.

Nominal Variables

Nominal variables categorise data without inherent order or ranking. These variables represent different categories or groups that are mutually exclusive and do not have a natural sequence.

Examples of nominal variables include gender, ethnicity, and blood type. For instance, gender can be classified as male, female, and non-binary. However, there is no inherent ranking between these categories.

Nominal variables classify data into distinct groups and are crucial for categorical data analysis. Statistical techniques like frequency tables, bar charts, and chi-square tests are commonly employed to analyse nominal variables. Understanding nominal variables helps researchers identify patterns and trends across different categories.

Ordinal Variables

Ordinal variables represent categories with a meaningful order or ranking, but the differences between the categories are not necessarily uniform or quantifiable. These variables provide information about the relative position of categories.

Examples of ordinal variables include education level (e.g., high school, bachelor's degree, master's degree) and customer satisfaction ratings (e.g., poor, fair, good, excellent). The categories have a specific order in these cases, but the exact distance between the ranks is not defined.

Ordinal variables are essential for analysing data where the order of categories matters, but the precise differences between categories are unknown. Researchers use ordinal scales to measure attitudes, preferences, and rankings. Statistical techniques such as median, percentiles, and ordinal logistic regression are employed to analyse ordinal data and understand the relative positioning of categories.

Comparison Between Quantitative and Qualitative Variables

Quantitative and qualitative variables serve different purposes and are analysed using distinct methods. Understanding their differences is essential for choosing the appropriate statistical techniques and drawing accurate conclusions.

Measurement: Quantitative variables are measured numerically and can be subjected to arithmetic operations, whereas qualitative variables are classified without numerical measurement.

Analysis Techniques: Quantitative variables are analysed using statistical methods like mean, standard deviation, and regression analysis, while qualitative variables are analysed using frequency distributions, chi-square tests, and non-parametric techniques.

Data Representation: Continuous and discrete variables are often represented using histograms, scatter plots, and box plots. Nominal and ordinal variables are defined using bar charts, pie charts, and frequency tables.

Frequently Asked Questions

What are the main types of variables in statistical analysis?

The main variables in statistical analysis are quantitative (continuous and discrete) and qualitative (nominal and ordinal). Quantitative variables involve measurable data, while qualitative variables categorise data without numerical measurement.

How do continuous and discrete variables differ? 

Continuous variables can take infinite values within a range and are measured precisely, such as height or temperature. Discrete variables, like the number of students, can only take specific, countable values and are not subdivisible.

What are nominal and ordinal variables in statistical analysis? 

Nominal variables categorise data into distinct groups without any inherent order, like gender or blood type. Ordinal variables involve categories with a meaningful order but unequal intervals, such as education levels or satisfaction ratings.

Conclusion

Understanding the types of variables in statistical analysis is crucial for accurate data interpretation. By distinguishing between quantitative variables (continuous and discrete) and qualitative variables (nominal and ordinal), researchers can select appropriate statistical methods and draw valid conclusions. This clarity enhances the quality and reliability of data-driven insights.

[tjeromebaker]

How to Use SPSS: A Beginner’s Guide

If you’re diving into the world of data analysis, SPSS (Statistical Package for the Social Sciences) is an essential tool to have in your arsenal. This guide walks you through the basics of SPSS to help you get started with confidence.

SPSS Statistics Essential Training If you’re diving into the world of data analysis, SPSS (Statistical Package for the Social Sciences) is an essential tool to have in your arsenal. This guide walks you through the basics of SPSS to help you get started with confidence. What is SPSS? SPSS is a powerful statistical software used by researchers, students, and professionals to manage, analyze,…

[unicodehealthcareservices45]

[reasonsforhope]

"People have been telling stories about renewable energy since the nineteen-seventies, when the first all-solar-powered house opened on the campus of the University of Delaware, drawing a hundred thousand visitors in 1973, its first year, to marvel at its early photovoltaic panels and its solar hot-water system, complete with salt tubs in the basement to store heat overnight. But, even though we’ve got used to seeing solar panels and wind turbines across the landscape in the intervening fifty years, we continue to think of what they produce as “alternative energy,” a supplement to the fossil-fuelled power that has run Western economies for more than two centuries. In the past two years, however, with surprisingly little notice, renewable energy has suddenly become the obvious, mainstream, cost-efficient choice around the world. Against all the big bad things happening on the planet (and despite all the best efforts of the Republican-led Congress in recent weeks), this is a very big and hopeful thing, which a short catalogue of recent numbers demonstrates:

It took from the invention of the photovoltaic solar cell, in 1954, until 2022 for the world to install a terawatt of solar power; the second terawatt came just two years later [in 2024], and the third will arrive either later this year or early next [in 2025 or early 2026].

That’s because people are now putting up a gigawatt’s worth of solar panels, the rough equivalent of the power generated by one coal-fired plant, every fifteen hours. Solar power is now growing faster than any power source in history, and it is closely followed by wind power—which is really another form of energy from the sun, since it is differential heating of the earth that produces the wind that turns the turbines.

Last year, ninety-six per cent of the global demand for new electricity was met by renewables, and in the United States ninety-three per cent of new generating capacity came from solar, wind, and an ever-increasing variety of batteries to store that power.

In March, for the first time, fossil fuels generated less than half the electricity in the U.S. In California, at one point on May 25th, renewables were producing a record hundred and fifty-eight per cent of the state’s power demand. Over the course of the entire day, they produced eighty-two per cent of the power in California, which, this spring, surpassed Japan to become the world’s fourth-largest economy.

Meanwhile, battery-storage capability has increased seventy-six per cent, based on this year’s projected estimates; at night, those batteries are often the main supplier of California’s electricity. As the director of reliability analysis at the North American Electric Reliability Corporation put it, in the CleanTechnica newsletter, “batteries can smooth out some of that variability from those times when the wind isn’t blowing or the sun isn’t shining.” As a result, California is so far using forty per cent less natural gas to generate electricity than it did in 2023, which is the single most hopeful statistic I’ve seen in four decades of writing about the climate crisis.

Texas is now installing renewable energy and batteries faster than California; in a single week in March, it set records for solar and wind production as well as for battery discharge. In May, when the state was hit by a near-record-breaking early-season heat wave, air-conditioners helped create a record demand on the grid, which didn’t blink—more than a quarter of the power came from the sun and wind. Last week’s flooding tragedy was a reminder of how vulnerable the state is to extreme weather, especially as water temperatures rise in the Gulf, producing more moisture in the air; in late June, the director of the state’s utility system said that the chances of emergency outages had dropped from sixteen per cent last summer to less than one per cent this year, mostly because the state had added ten thousand megawatts of solar power and battery storage. That, he said, “puts us in a better position.”

All this is dwarfed by what’s happening in China, which currently installs more than half the world’s renewable energy and storage within its own borders, and exports most of the solar panels and batteries used by the rest of the world. In May, according to government records, China had installed a record ninety-three gigawatts of solar power—amounting to a gigawatt every eight hours. The pace was apparently paying off—analysts reported that, in the first quarter of the year, total carbon emissions in China had actually decreased; emissions linked to producing electricity fell nearly six per cent, as solar and wind have replaced coal. In 2024, almost half the automobiles sold in China, which is the world’s largest car market, were full or hybrid electric vehicles. And China’s prowess at producing cheap solar panels (and E.V.s) means that nations with which it has strong trading links—in Asia, Africa, South America—are seeing their own surge of renewable power.

In South America, for example, where a decade ago there were plans to build fifteen new coal-fired power plants, as of this spring there are none. There’s better news yet from India, now the world’s fastest-growing major economy and most populous nation, where data last month showed that from January through April a surge in solar production kept the country’s coal use flat and also cut the amount of natural gas used during the same period in 2024 by a quarter. But even countries far from Beijing are making quick shifts. Poland—long a leading coal-mining nation—saw renewable power outstrip coal for electric generation in May, thanks to a remarkable surge in solar construction. In 2021, the country set a goal for photovoltaic power usage by 2030; it has already tripled that goal.

Over the past fifteen years, the Chinese became so skilled at building batteries—first for cellphones, then cars, and now for entire electric systems—that the cost of energy storage has dropped ninety-five per cent. On July 7th, a round of bidding between battery companies to provide storage for Chinese utilities showed another thirty per cent drop in price. Grid-scale batteries have become so large that they can power whole cities for hours at a time; in 2025, the world will add eighty gigawatts of grid-scale storage, an eightfold increase from 2021. The U.S. alone put up four gigawatts of storage in the first half of 2024.

There are lots of other technologies vying to replace fossil fuels or to reduce climate damage: nuclear power, hydrogen power, carbon capture and storage; along with renewables, all were boosted by spending provisions in Biden’s Inflation Reduction Act and will be hampered to varying degrees by congressional rollbacks. Some may prove useful in the long run and others illusory, but for now they are statistically swamped by the sheer amount of renewable power coming online. Globally, roughly a third more power is being generated from the sun this spring than last. If this exponential rate of growth can continue, we will soon live in a very different world.

All this suggests that there is a chance for a deep reordering of the earth’s power systems, in every sense of the word “power,” offering a plausible check to not only the climate crisis but to autocracy. Instead of relying on scattered deposits of fossil fuel—the control of which has largely defined geopolitics for more than a century—we are moving rapidly toward a reliance on diffuse but ubiquitous sources of supply. The sun and the wind are available everywhere, and they complement each other well; when sunlight diminishes in the northern latitudes at the approach of winter, the winds pick up. This energy is impossible to hoard and difficult to fight wars over. If you’re interested in abundance, the sun beams tens of thousands of times more energy at the earth than we currently need. Paradigm shifts like this don’t come along often: the Industrial Revolution, the computer revolution. But, when they do, they change the world in profound and unpredictable ways...

In retrospect, it’s reasonably easy to see how fast solar and wind power were coming. But, blinkered by the status quo, almost no one actually predicted it. In 2009, the International Energy Agency predicted that we would hit two hundred and forty-four gigawatts of solar capacity by 2030; we hit it by 2015. For most of the past decade, the I.E.A.’s five-year forecasts missed [underestimated the amount of renewables] by an average of two hundred and thirty-five per cent. The only group that came even remotely close to getting it right was not J. P. Morgan Chase or Dow Jones or BlackRock. It was Greenpeace, which estimated in 2009 that we’d hit nine hundred and twenty-one total gigawatts by 2030. We were more than fifty per cent above that by 2023. Last summer, Jenny Chase, who has been tracking the economics of solar power for more than two decades for Bloomberg, told the Times, “If you’d told me nearly 20 years ago what would be the case now, 20 years later, I would have just said you were crazy. I would have laughed in your face. There is genuinely a revolution happening.”

-via The New Yorker, July 9, 2025

[todays-xkcd]

If you think curiosity without rigor is bad, you should see rigor without curiosity.

Good Science [Explained]

Transcript Under the Cut

[Miss Lenhart is standing in front of a whiteboard with some scribbles on it.] Miss Lenhart: I'm supposed to give you the tools to do good science.

[Miss Lenhart is now standing in front of Jill and Cueball, who are seated at classroom desks.] Miss Lenhart: But what are those tools? Miss Lenhart: Methodology is hard and there are so many ways to get incorrect results. Miss Lenhart: What is the magic ingredient that makes for good science?

[Miss Lenhart headshot.] Miss Lenhart: To figure it out, I ran a regression with all the factors people say are important:

[A list, presented in a sub-panel that Miss Lenhart is pointing to:] Outcome variable: • correct scientific results

Predictors: • collaboration • skepticism of others' claims • questioning your own beliefs • trying to falsify hypotheses • checking citations • statistical rigor • blinded analysis • financial disclosure • open data [presumably the list goes on, as it runs off the visible part of the panel]

[Another Miss Lenhart headshot.] Miss Lenhart: The regression says two ingredients are the most crucial: 1) genuine curiosity about the answer to a question, and 2) ammonium hydroxide

[Miss Lenhart, standing, and Jill, seated at desk] Jill: Wait, why did ammonia score so high? How did it even get on the list? Miss Lenhart: ...and now you're doing good science!

[tenth-sentence]

Pearson was dealing with so-called nominal variables – that is, with discontinuously distributed data.

"In the Name of Eugenics: Genetics and the Uses of Human Heredity" - Daniel J. Kevles

[diamonddaze01]

Error 404: Feelings not Found

pairing: jeon wonwoo x f!reader | wc: 4.0k genre: fluff, electrical engineering student wonwoo (pulled out my textbooks for this) warnings: loserboy core a/n: for all my fellow left-brained girlies who have never really understood feelings. sometimes, all you have to do is feel // now playing: when he sees me // thank u kae @ylangelegy for the song suggestion and betaing ily muah!

summary: Wonwoo has always been comfortable in the world of logic.  But his crush on you? A catastrophic anomaly in his otherwise perfectly functioning system.

Wonwoo has always been comfortable in the world of logic. Numbers are predictable, formulas are consistent, and circuits behave exactly as they’re supposed to. But his crush on you? A catastrophic anomaly in his otherwise perfectly functioning system.

It’s not like he planned for this. (Wonwoo plans for everything.) He planned how to tackle his midterms, down to how much coffee he’d need for optimal brain function. He planned his study schedule for finals week with a level of precision that could rival NASA’s launch timelines. But he didn’t plan for you—didn’t account for how you’d waltz into his life, smiling like it was easy, and throw every variable he’d ever known into disarray.

Take last week, for instance. You’d borrowed his notes in Signals class after the professor’s lecture turned into a chaotic sprint of equations, leaving most of the class scrambling to catch up. Wonwoo’s notes, as always, were pristine—straight lines, perfect margins, not a single smudge or scribble.

“These are amazing,” you’d said, eyes scanning the page before handing them back. “Your designs are so clean.”

Simple, right? A harmless comment. But by the time he’s back at his desk, staring at his notebook, the words replay in his mind like an unsolved equation. Somewhere between “clean” and the way you smiled, his brain spins out of control, dragging him into an entirely unnecessary analysis.

By the time the clock strikes midnight, he’s halfway through a list of possible interpretations for the word clean.

Did you mean clean as in technically proficient?

Or was it a general observation, like, “Oh, clean lines, nice work”?

Was it just a filler compliment?

Wait, what if you didn’t care about the project at all and were just being polite?

…Or were you flirting?

By the end of the day, the list has ballooned to 27 points, each item meticulously numbered and annotated with follow-up questions. He’s considered:

The tone of your voice (friendly, teasing, or something else entirely?).

The duration of eye contact (exactly 2.3 seconds—long enough to register intent?).

The statistical likelihood of romantic interest based on casual interactions in a shared academic setting.

He even creates a small flowchart titled “Compliment Probability Breakdown” in the margins, complete with arrows leading to various outcomes: “Casual comment” → “Friendly disposition” → “No further analysis needed.” Except, of course, he does further analyze. He always further analyzes.

Mingyu finds him later that night, still hunched over the notebook with a pencil tucked behind his ear. “Wonwoo, what are you doing? It’s a compliment, man. Just take it.”

Wonwoo glares up at him, a little defensive. “Compliments can have layers.”

“Compliments are not onions, dude. Sometimes people just say stuff because they mean it.” Mingyu grabs the notebook, flipping through pages of scribbled notes and diagrams. “Wait, are you seriously tracking eye contact now?”

Wonwoo snatches it back with a huff. “It’s for clarity.”

“Clarity,” Mingyu repeats, shaking his head. “Okay, listen: not everything needs a breakdown. Maybe she just thinks you’re good at this stuff.”

The suggestion should feel reassuring, but it only creates more questions. Do you think he’s good at this stuff? Wonwoo’s chest tightens as the overanalysis starts up again, his brain racing to decode every minor interaction between you two.

And for the first time in his life, he wonders if there’s a problem even logic can’t solve.

The first time Wonwoo realizes he might have a crush on you is during a Circuits lab. The task is simple: build an EKG circuit. The professor’s voice echoes in the background, laying out the steps, but Wonwoo doesn’t need instructions—he’s already ahead, mentally piecing together the circuit in his mind like a jigsaw puzzle.

You, him, and Soonyoung are grouped together. Soonyoung, true to form, spends more time spinning a pen between his fingers and accidentally dropping it than actually contributing. “What’s a diode again?” he whispers, squinting at the diagram. Wonwoo doesn’t bother answering. He’s focused on soldering the components, the familiar rhythm of it calming.

Then you lean closer. Close enough that he catches the faint scent of your shampoo—something floral, light, completely unexpected.

“Wow, you’re fast,” you say as Wonwoo expertly attaches a capacitor to the circuit. There’s a trace of genuine admiration in your voice, enough to make him falter. “I’d probably still be looking for the resistor.”

The comment shouldn’t faze him. It’s just a compliment, nothing extraordinary. He glances at you, briefly, before immediately looking back at the board. It feels safer not to meet your eyes for too long. “Uh, it’s color-coded,” he manages, his voice steady but quieter than usual. “You just… follow the stripes.”

You laugh softly, the sound threading its way into his chest like a loose wire connecting where it shouldn’t. “Yeah, but it’s not that simple for everyone,” you say, brushing a stray hair out of your face as you turn your attention to the circuit.

The way you say it makes his chest feel strangely tight—like you’ve taken something as mundane as resistors and turned it into a compliment, like you’re saying he’s not simple either. It’s a ridiculous thought, and yet it roots itself in his mind.

Wonwoo’s hand, soldering iron poised mid-air, doesn’t move. His brain, which usually fires on all cylinders, freezes like an overloaded processor. The soldering iron hovers dangerously close to the board, but all he can focus on is the way your hair catches the light, the way your fingers curl around the resistor as you inspect it. Wonwoo doesn’t mean to notice, but suddenly he can’t stop noticing—the way the fluorescent light reflects in your eyes, the faint trace of soap on your hands when you adjust a wire, the warmth radiating from your voice when you hum quietly in thought.

It’s not until Soonyoung gently clears his throat that he realizes his brain has completely stopped functioning. His usually razor-sharp focus is now cluttered with incoherent static. 

“Wonwoo?” you ask, leaning back slightly to meet his eyes. There’s a hint of concern in your voice. “You good?”

He panics. “Uh. 100 ohms.”

Your brow furrows. “What?”

“Uh—100 ohms,” he repeats, gesturing vaguely at the resistor in your hand like it explains anything. “That’s… its resistance.”

There’s a beat of silence, thick and awkward. You blink at him, clearly trying to piece together whatever he’s just said. Then you burst out laughing, shaking your head as you turn back to the project. “Okay, resistor boy. Whatever you say.”

The sound of your laughter leaves his chest feeling tight, like someone’s replaced his heart with a capacitor about to blow.

Soonyoung, who’s been watching the exchange with far too much interest, smirks. He leans over the table, stage-whispering, “What was that?”

“What was what?” Wonwoo mutters, focusing on the soldering again, as if he can undo the entire exchange by sheer force of will.

“You’re usually all cool and robotic,” Soonyoung teases, wagging his pen like it’s some kind of magic wand. “That was… weird.”

Wonwoo shakes his head quickly, but the heat creeping up the back of his neck says otherwise. “I don’t know,” he mumbles, the words barely audible over the hum of the soldering iron. “I think I glitched.”

“Uh, yeah. Glitched hard.” Soonyoung grins, nudging him in the ribs. “Man, this is going to be fun to watch.”

Wonwoo groans, his ears burning. The circuit in front of him makes perfect sense—the resistors, the capacitors, the impedance of the op-amp—but nothing about you fits into a neat schematic. And for the first time in his life, that terrifies him.

Now, weeks later, Wonwoo is in his room, utterly consumed by the mess on his desk. It’s an anomaly in itself—Wonwoo is meticulous, his workspace usually a shrine to organization (he always says: clean desk, clean mind). But now, papers are scattered like fallen leaves, covered in scribbles, equations, and bullet points that grow increasingly frantic as they spread across the desk.

The centerpiece of this chaos? A flowchart spanning two pages, taped together like some sort of grand engineering blueprint. It’s titled, in block letters: “Signs She Might Like Me Back.”

Wonwoo taps his pen against the paper, staring at the branching lines as if sheer focus might make them reveal the answer he’s been agonizing over. Beneath the title are subcategories labeled “Physical Cues,” “Verbal Indicators,” and, his personal favorite, “Ambiguous Behavior That Could Go Either Way.”

Under “Physical Cues,” he’s written:

Smiles when she sees me.

Leans closer during conversation (but what if it’s because of background noise?).

Touches my arm (happened once, inconclusive).

Under “Verbal Indicators,” there’s a bullet that reads:

Complimented my handwriting. Significance unclear.

He’s in the middle of adding a new branch—“Initiates conversation (specific or casual?)”—when the door bursts open without warning.

“Wonwoo, what the hell are you doing? It’s 3 AM.” Mingyu strides in, holding a bowl of instant ramen and a look of mild concern. His gaze lands on the desk, and his expression shifts to outright amusement. “Wait… what is this?”

Wonwoo freezes like he’s been caught committing a federal crime. He instinctively moves to cover the flowchart with both arms, but it’s far too late. Mingyu steps closer, craning his neck to read the edges of the paper that Wonwoo couldn’t shield in time.

“‘Compliments: Genuine or Polite’?” Mingyu reads aloud, his voice rising in barely-contained glee. He sets the ramen down and leans over the desk. “‘Smiles frequently—friendly or flirty?’ Wonwoo…” He looks at his friend, wide-eyed and grinning. “Are you seriously trying to analyze feelings right now?”

“No,” Wonwoo lies, far too quickly. “It’s… theoretical.”

Mingyu snorts, dropping into the chair beside him and spinning it halfway around before leaning forward. “Theoretical? Dude, this looks like the final project for your psych elective. Come on, what’s the problem? Spill.”

Wonwoo hesitates, gripping his pen like it’s the only thing tethering him to reality. But the weight of weeks of overthinking finally tips the scale, and he lets out a long sigh, setting the pen down.

“I just don’t… get it,” he admits, gesturing vaguely to the papers. “Feelings are so inconsistent. They don’t follow any rules. There’s no formula to predict intent, no way to be certain what someone means. How do people know if someone’s interested in them? How do you know when to… I don’t know, do something about it?”

Mingyu leans back in the chair, arms crossed as he considers the question. “Easy,” he says after a beat. “You stop thinking about it so much and just ask them out.”

Wonwoo blinks at him, utterly horrified. “That’s… illogical. That’s guessing. That’s like building a circuit without testing the components first. What if the whole thing explodes?”

“Yeah, well, feelings aren’t supposed to be logical,” Mingyu says with a shrug, grabbing the bowl of ramen and slurping a mouthful. He claps Wonwoo on the shoulder with his free hand, grinning around his chopsticks. “Face it, man. You’re screwed.”

Wonwoo stares at him, expression blank but mind racing at a million miles an hour. “There’s got to be a better way than just… guessing.”

“Good luck finding it,” Mingyu says, standing up and taking his ramen with him. “But if you don’t make a move soon, she might just think you’re not interested. So, you know… keep that in mind.”

Wonwoo sits in silence long after Mingyu leaves, staring down at his flowchart. His pen hovers over the paper, but he doesn’t write anything. For once, the calculations feel insufficient.

And maybe, just maybe, Mingyu’s right.

The thing is, you keep throwing off his system. Wonwoo’s world is built on rules, a place where inputs lead to predictable outputs. But you? You’re the glitch in his perfectly functioning program, an anomaly he can’t solve no matter how many late nights he spends overanalyzing.

The way you laugh at his deadpan jokes—it’s too loud for the library but not loud enough to draw attention, just enough to pull his gaze toward you. It doesn’t matter that you’ve already heard that joke during last week’s study session; you laugh anyway, and the sound is unreasonably addictive. The way you ask for help even when he knows you don’t need it. Like last week, when you slid your notebook toward him with a confused pout.

“Can you help me with this? I don’t get it.”

He barely glanced at the equation. “You’re way too smart to not understand this.”

And then you laughed, a soft, warm sound that curled around his chest and lodged itself there. That laugh earned a solid 15 points on his internal ‘Possible Signs of Interest’ checklist, though he later downgraded it to 10 because he couldn’t account for external variables like your naturally kind disposition.

It’s infuriating. Why do feelings refuse to conform to logic?

He tries analyzing every interaction, mapping out probabilities and outcomes in the quiet corners of his mind. He’s drawn tables, diagrams, even flowcharts in an attempt to parse out the truth.

Was the way you leaned closer during study group last week a sign of interest? Or were you just trying to hear him better? Did the way you laughed at his dumb, offhand comment in class mean something? Or do you just laugh like that at everything?

Take today, for example: You brushed past him on your way to class, smiling and throwing over your shoulder, “See you at study group later!” That brief moment derailed his entire afternoon.

Did you linger when your arm touched his? Or was that just an accidental graze? Was your smile just friendly, or something more?

And why does he care so much?

Wonwoo spends the rest of the day distracted, his mind looping through possibilities like an endless algorithm stuck in an infinite while-loop. What’s worse is that he doesn’t even know what he wants the answer to be. A part of him craves certainty, some definitive sign that he should act on these feelings. But another part—a quieter, more cautious part—fears the idea of ruining the tenuous balance between you two.

Because what if he’s wrong? What if you’re just like this with everyone? What if he makes his move and you pull away, looking at him like he’s a problem to be solved instead of someone you enjoy spending time with?

By the time the study session rolls around, he’s teetering on the edge of complete disarray, not that he’d ever let it show.

Or so he thinks.

Because two hours in, he miscalculates an integral. An integral. Wonwoo never miscalculates anything.

You catch it immediately, tilting your head as you lean closer. He can feel the heat radiating off your skin, the soft rustle of your notebook as you shift it toward him.

“Are you okay, Wonwoo? You’re usually so precise,” you say, your voice light but with an edge of curiosity.

His ears burn. “Just tired,” he mumbles, avoiding your gaze as he corrects the mistake. He doesn’t add that it’s your proximity short-circuiting his brain, or that the way your hair falls over your shoulder is infinitely more distracting than any differential equation.

Your smirk lingers in his periphery, and he wonders if you can tell just how fast his heart is beating. He wonders if you feel the same strange, unexplainable pull that he does.

The study session stretches late into the evening. Most of the group has already packed up, and you’re the last one still typing away at your laptop when Wonwoo’s caffeine miscalculation finally catches up to him.

He doesn’t remember falling asleep—just the faint hum of your keyboard and the warm glow of the desk lamp. When he stirs slightly, he feels a ghosting touch against his face.

Your fingers are gentle as you slide his glasses off, careful not to wake him. He feels the cool metal leave his skin, followed by the soft brush of your thumb near the mark his nose pad left.

His heart lurches, and he has to force himself to keep his breathing even. A dozen thoughts rush through his mind all at once:

Is she doing this because she likes me?No, she’s just being considerate.But she’s touching my face.What does that mean? What does it mean if she’s touching my face?

He clenches his fists against the urge to open his eyes, to meet your gaze and demand answers. Instead, he forces himself to focus on the moment—the sound of your quiet breaths, the occasional click of your mouse, and the warmth that radiates from your side of the table.

For a fleeting moment, he thinks: Maybe emotions don’t always need to make sense. Maybe, just this once, he can let go of the need to understand everything.

Maybe, just this once, he can let himself feel.

Wonwoo doesn’t know how it’s come to this. One moment, he was perfectly content at home, considering a quiet evening spent debugging code or reorganizing his bookshelves. The next, Mingyu and Soonyoung were in his room, looming like conspirators with matching grins.

“You have to come,” Mingyu had said, tugging at the sleeves of Wonwoo’s sweatshirt. “It’s social interaction, it’s good for you. You’ll thank us later.”

“No, I won’t,” Wonwoo deadpanned, crossing his arms.

Soonyoung leaned in, holding up his phone with a smug look. “You sure about that? Because I might have accidentally taken a picture of that Venn diagram you made the other day.”

Wonwoo froze, his blood running cold. “You wouldn’t.”

“Oh, but I would.” Soonyoung’s grin widened. “And I bet someone would find it very… interesting.”

That was how he found himself lacing up his sneakers with a grim expression, muttering under his breath about betrayal and bad friends.

Now, standing awkwardly at the edge of a crowded house party, Wonwoo is reminded why he hates these things. The music is too loud, the lights are too dim, and there are far too many people moving unpredictably around him. He’s already considering texting Mingyu and Soonyoung to demand their exact location when he spots you.

You’re standing by the makeshift bar, laughing at something someone said, your smile so effortless it lights up the room in a way the cheap string lights never could. Wonwoo doesn’t mean to stare, but his feet move before his brain can catch up. He tells himself it’s because you’re familiar, a safe point of contact in an otherwise chaotic environment.

But deep down, he knows better.

“Wonwoo?” you call out, your eyes lighting up as you notice him approaching from the edge of the room.

He halts mid-step, caught somewhere between relief and apprehension, and forces out a casual, “Hey.” His hands disappear into his pockets, his fingers fidgeting with loose threads, unsure what else to do.

You grin, leaning one elbow against the counter, your drink swaying lazily in your other hand. “You don’t seem like the party type,” you tease, tilting your head to study him.

“I was... coerced,” he replies flatly, and the corner of your mouth quirks up as you laugh.

“Oh, let me guess.” You raise an eyebrow, pretending to think hard. “Mingyu? No, no—Soonyoung. Or both? Definitely both.”

“They’re... relentless,” Wonwoo admits, almost sounding offended, but there’s a faint twitch of a smile at the edges of his lips.

“Wow. Dragged out of your hobbit hole just to stand here and glare at people? They must’ve bribed you with something really good.”

He looks away, almost sheepishly. “Something like that.”

Your laugh rings out again, easy and unforced, and Wonwoo feels a little lighter despite himself. “Poor you,” you say, your voice dripping with mock sympathy. “Do you need a drink to cope? A strong one?”

He snorts. “I’m fine, thanks.”

“Well, you made it out of the house, so I guess that’s something,” you say, stepping closer. “Though you do look like you’re two minutes away from bolting.”

He shrugs, his gaze flickering between you and the crowd. “It’s not my scene.”

“And yet, here you are,” you point out, your tone playful. “Is it for Mingyu? Or Soonyoung? Or…” You pause, a slow smile spreading across your face. “...someone else?”

His brain short-circuits at your words, but he does his best to play it cool. “I think they just wanted to ruin my night.”

“Hmm,” you hum, unconvinced but amused. “Well, I’m glad you’re here. It’s always fun seeing you outside your natural habitat. Like spotting a rare Pokémon.”

“Am I supposed to thank you for that?” he asks dryly, and you grin.

The two of you ease into conversation, the party blurring into background noise as you chat. Wonwoo listens intently, hanging onto your every word as if your voice alone could drown out the overwhelming din around him. He’s not even sure how much time has passed when you lean a little closer, the shift in your tone catching his attention.

“So,” you say, a conspiratorial grin tugging at your lips. “Do you have anyone you’re crushing on?”

He freezes. The words settle in his chest like a sudden, unsteady weight.

Does he? Of course, he does—you. But his brain stalls, caught between the truth and the absolute terror of saying it out loud. Instead of answering, he scrambles for something—anything—to say.

“I’m going to make an app,” he blurts out, the words tumbling from his mouth before he can stop them.

You blink, tilting your head. “An app?”

He nods, trying to steady his voice even though his heart feels like it’s about to burst. “Feelings confuse me. So I’m taking all the data I’ve collected and making an app to tell if someone’s interested. Algorithms are easier for me to understand, anyway.”

Your expression flickers between confusion and amusement before a slow smirk spreads across your face. “What data, Wonwoo?” you ask, setting your drink down and stepping closer.

His throat goes dry. “I—I didn’t mean—”

“Because if you’ve been collecting data,” you continue, your voice teasing as you close the distance between you, “I’d love to hear about it. What have you noticed?”

His pulse skyrockets as you reach for his hands, gently guiding them to rest on your waist. The warmth of your touch sends his mind spiraling, and for a moment, he forgets how to breathe. Your hands slide behind his neck, your fingers brushing against the sensitive skin there, and he feels like he’s standing on the edge of a cliff.

“I don’t know how much more obvious I could have been,” you murmur, your teasing tone softening into something warmer, more certain.

His mind blanks. He should say something—anything—but all he can do is stare at you, completely undone.

Then you lean in, your lips brushing against his, tentative at first, as if waiting for him to meet you halfway. And when he does—hesitant but earnest—you smile into the kiss, your fingers tangling gently in his hair, and it feels like the world stops spinning.

For Wonwoo, everything finally clicks.

It’s not a Venn diagram or a flowchart, and it doesn’t follow any logical formula, but it makes sense in a way he can’t explain. The way your hands fit behind his neck, the warmth of your body against his, the soft sigh that escapes you when his hands tighten on your waist—it’s all the proof he needs.

When you pull back, his head is spinning, but you’re still close, your breath mingling with his.

“So,” you say, your tone light but your eyes impossibly warm. “Do you still need that app?”

He chuckles softly, the sound unsteady but genuine. “No,” he admits, a small, shy smile tugging at his lips. “I think I’ve got all the data I need.”

You laugh, and the sound is music to his ears. For the first time in weeks—months, even—Wonwoo feels like he can stop overthinking, stop analyzing every little detail. He doesn’t need an algorithm, a chart, or a diagram to tell him what’s in front of him. Because some things don’t need to be solved.

Some things just need to be felt.

[maeintree]

first kiss statistics | s. reid

Summary: Spencer Reid can’t help but overanalyze, especially when it comes to new experiences. As the moment between you two grows more charged, he dives into a detailed breakdown of first kisses, but before he can get too far into the statistics, you decide to take matters into your own hands. Pairing: Spencer Reid x Reader Word Count: 1.1k Warnings: Fluff, light kissing, and suggestive sexual themes. Author's Note: jus some small fluff to get me started throughout the day! wrote this on the bus so forgive me if the writing is a 'lil ehhh. nevertheless, enjoy <3

Spencer Reid had a tendency to overanalyze, especially when it came to things like numbers, probabilities, and, as you quickly learned, emotions.

You had spent countless hours together—solving cases, sharing stories, laughing at random trivia—but the air between you two had started to shift. The way his eyes lingered a little longer on you, the quiet smiles, and how he’d look at you when he thought you weren’t paying attention.

It wasn’t that you didn’t know what was going on. You had been in the same boat for a while now, both dancing around the undeniable pull between you two. But Spencer being Spencer, it was only a matter of time before he tried to make sense of it all—calculated it down to the very last decimal.

And tonight, it seemed, was that night.

The two of you were sitting on the couch in his apartment, a case from the day still fresh in your mind. The distant sound of the TV playing was barely noticeable in the background.

Spencer had been rambling on about the latest book he’d read, something about quantum physics, when he suddenly quieted, his gaze shifting from the pages of his book to you. The space between you seemed impossibly small, yet neither of you moved.

You could feel the tension in the air—both of you were trying to navigate this unspoken thing, but neither of you knew where to begin. You glanced down at your lap, fingers fiddling nervously, before you felt the soft brush of Spencer’s knee against yours.

The light touch, so innocent and casual, made your heart beat a little faster.

“So,” Spencer began, his voice tentative as if he were still unsure of how to broach the topic, “have you ever heard of the psychology behind first kisses?”

You raised an eyebrow, shifting to face him fully. “Spencer, are you really going to lecture me on first kisses?”

His lips twitched in that half-smile you’d come to adore, but there was an unmistakable tension in his shoulders. “No, it’s just... well, the first kiss is crucial. There’s a whole branch of research on it—on how it affects the likelihood of long-term compatibility, how it can set the tone for the entire relationship.”

You tilted your head, already suspecting where this conversation might go. “And what does the research say, Doctor Reid?”

He paused for a moment, considering, before launching into one of his signature monologues.

“Well, according to a study from the University of Michigan, there’s a 70% correlation between a positive first kiss and the success of a relationship. That’s a pretty high percentage, considering there’s so much that could go wrong. Lip pressure, angle, timing... There’s also a study by Dr. Justin Lehmiller that suggests kissing with passion can create a chemical reaction—dopamine and oxytocin—which, in theory, should make us feel more connected to each other.”

You had to bite back a smile.

Spencer Reid. His brain working overtime, analyzing everything, even when the situation didn’t need analysis.

The more he talked, the more you could see the wheels turning behind his eyes, his expression becoming more and more absorbed in the science of it all.

“But,” he continued, completely unaware of the amused smile creeping onto your face, “there are a number of variables. For example, the timing of the kiss, the level of comfort between the partners, and—”

You couldn’t take it anymore. Spencer was too cute, too wrapped up in his own thoughts, and you needed to snap him out of it before he started bringing up the various angles and kissing techniques again. You reached out, placing your finger gently over his lips to stop his rambling.

“Spencer,” you said, your voice low but firm, “can you just... stop?”

He blinked, caught off guard by the sudden interruption. “Stop?”

“Stop thinking so much,” you said with a soft laugh. “Just for a second.”

His eyes widened, a flicker of confusion crossing his face. “I—I don’t know how to not think, matter of fact, that's impossibl—”

You interrupt him. “Then just feel.” You inched closer, your heart pounding in your chest as you closed the distance.

His eyes darted between your lips and your eyes, his breath quickening, and you could tell he was still trying to calculate the probability of what might happen next.

Before he could say anything else, you leaned in and pressed your lips to his, cutting off his analysis entirely.

At first, Spencer was frozen—his body stiff as though he couldn’t quite comprehend what was happening. But then, slowly, tentatively, his lips began to move against yours, a gentle and cautious touch that spoke of everything he hadn’t said yet.

His hand hovered beside you for a moment before gently resting against your shoulder, his fingers brushing your skin.

The kiss was everything you imagined and nothing like what you expected. It wasn’t about probabilities or perfect techniques. It was raw, unfiltered, and real. It was messy in the best possible way, with your hearts beating in sync and everything around you fading into the background.

When you finally pulled away, you could feel the heat on your cheeks. Spencer’s eyes were wide, blinking as if trying to catch up to the moment. His breath was shaky, and his lips parted slightly as though he were still processing the kiss.

“I... uh,” he stammered, trying to find his words, “I didn’t... I didn’t factor in the emotional connection, the—”

You chuckled softly, brushing a strand of hair behind your ear. “Spencer, I swear to God, if you bring up another statistic right now, I’m going to kiss you again to stop you.”

His eyes widened, a flash of realization crossing his face. “Wait—what do you—”

Before he could say anything else, you stood up and, without a word, slid onto his lap. Spencer froze for a moment, eyes wide as he processed the sudden change, but then his hands instinctively settled on your waist.

His breath hitched as you leaned in, your lips meeting his once again, this time with more intensity.

You deepened the kiss, your hands threading into his hair as you pulled him closer. Spencer’s hands tightened around you, and you could feel the nervous energy melting away as he kissed you back, fully present—just the feeling of you in his arms. The kiss grew more urgent, more passionate, as though neither of you could wait any longer.

When you finally broke away, both of you were breathless, your hearts racing. Spencer’s face was flushed, his lips swollen from the kiss, and his eyes shone with a mixture of surprise and contentment.

“I guess I was right,” he whispered, his voice a little hoarse.

“About what?” you asked, still resting against him, feeling the warmth of his embrace.

“That some things... don’t need to be calculated,” he said with a smile, his hands gently caressing your back.

You grinned, pressing another soft kiss to his lips.

"Good."

hope you enjoyed this fluffy fic. writing this made me happy and i hope you reading it will too :) likes, reposts, and comments are much appreciated!

[covid-safer-hotties]

This groundbreaking study finds that you are about 150% more likely to cause/take part in a traffic accident following a covid infection. This increase is across the board with no effect from vaccination or long covid status. The authors say this is likely due to neurological changes in anyone post covid infection. Mask up. Don't let a virus rewire your brain.

Abstract Objective This study evaluated the association between acute COVID-19 cases and the number of car crashes with varying COVID-19 vaccination rates, Long COVID rates, and COVID-19 mitigation strategies.

Background The ongoing SARS-CoV-2 pandemic has led to significant concern over long-term post-infection sequelae, especially in the Neurologic domain. Long COVID symptoms, including cognitive impairments, could potentially impact activities requiring high cognitive function, such as driving. Despite various potential impacts on driving skills and the general prevalence of Long COVID, the specific effects on driving capabilities remain understudied.

Design/Methods This study utilized a Poisson regression model to analyze data from 2020-2022, comparing aggregate car crash records and COVID-19 statistics. This model adjusted for population and included binary variables for specific months to account for stay-at-home orders. The correlation between acute COVID-19 cases and car crashes was investigated across seven states, considering vaccination rates and COVID-19 mitigation measures as potential confounders.

Results Findings indicate an association between acute COVID-19 rates and increased car crashes with an OR of 1.5 (1.23-1.26 95%CI). The analysis did not find a protective effect of vaccination against increased crash risks, contrary to previous assumptions. The OR of car crashes associated with COVID-19 was comparable to driving under the influence of alcohol at legal limits or driving with a seizure disorder.

Conclusions The study suggests that acute COVID-19, regardless of Long COVID status, is linked to an increased risk of car crashes presumably due to neurologic changes caused by SARS-CoV-2. These findings underscore the need for further research into the neuropsychological impacts of COVID-19. Further studies are recommended to explore the causality and mechanisms behind these findings and to evaluate the implications for public safety in other critical operational tasks. Finally, neurologists dealing with post-COVID patients, should remember that they may have an obligation to report medically impaired drivers.

[saesbangs]

concert.

itoshi sae x fem idol!reader

part of this series.

(y/n): hey!

(y/n): my concert next month

(y/n): saved you a ticket! if you don't come and watch i'd be actually sad TvT

(y/n): see u there!!

(y/n) sent you a file.

His phone buzzed with notifications in the middle of his session analyzing Blue Lock's data, considering them as new contender for the world stage. A small hum went out his throat as he read through your messages.

A concert.

He was never one to be attending such events. But it's Your concert, this one. Not to mention he has all of your songs in his go-to playlist, thus accepting the invitation might not be a bad idea. Contemplating, he checked on his calendar.

Not one agenda yet.

That date was as free as it gets. As if fate itself allowing him to attend. Without putting more thoughts, he typed in.

XX April 2019 - (Y/n)'s Concert.

For a bit, he stared at it blankly. Wondering how does one measure if an idol is the best out of the competition. For athletes, there is clear statistics and objectives. So measuring one's success is not too hard of a math. However, there seem to be lots of variables when it comes to an artist's success equation. Even putting quantitative index on those factors is not something definitive. His brain worked overtime trying to figure this out in mere minutes.

Reaching the dead end, he brushed it off, placing his phone back on the table. He shifted his attention back towards his previous analysis after mumbling to himself,

"How do you plan to know when you've fulfilled your promise, (Y/n)?"

[lorelune]

aventurine with a reader who is his handler. your primary job? risk analysis. you were an intelligentsia guild member-- once, before your talent for mental statistical computations were fully discovered. being quietly brilliant was much easier than being loudly so. where you could once toil away on private research on the ipc's dime, you now trail behind aventurine, attempting to mitigate all the damage that ripples around him.

(this is particularly difficult as aventurine is a man cursed with luck so good that it's a statistical anomaly. prediction is useless. calculations must be made on the fly and you must pray you are accurate, lest the strategic investment department end up in some amount of personal of fiscal debt themselves.)

aventurine had assured you initially that you didn't need to keep such a close eye on him. and at first, you'd believed him. he is one of the ten stonehearts, and well-regarded despite the rumors and brand on his neck. it's-- it's not your business anyway. to pry. you trust him.

and truthfully, he does keep a good handle on himself. he gets out of all of his gambles in one-- piece. sort of. he either skirts disaster with no room to spare or he takes on the disaster with his own two hands and grit and fucking wins.

and truthfully, if that was the only thing you had to analyze about aventurine, your job would be quite easy. he's lucky. he wins.

however-- there's just so much more to it than that. factors and variables that aren't affected by aventurine's uniquely good fortune. there always is. but what is and what isn't is hard to suss out. it-- it all constantly changes and hence you have to be in aventurine's shadow and hope that your mind is fast enough to deduce and calculate at the speed that aventurine cuts typical odds down to aventurine odds.

which is to say, that exhaustion follows in your shadow.

aventurine isn't a horrible boss. as much as you're his handler, he's yours. there's a semi-silent, mutual duty you both carry. aventurine makes sure you stay in his shadow, just out of sight and out of danger (so, he can position himself in front of any bullets, stray or otherwise. because they will never hit him.) and you make sure that he does not inadvertently cause a firestorm half a galaxy away.

it works. it's tenuous, most of the time. because aventurine thinks getting close to you is his greatest gamble (one cannot use luck to mend a broken heart). and because you recognize that, for all of your risk analysis and statistical understanding of the universe at large, at some point, you will be in aventurine's wake at the wrong time. and your luck, in conjunction to his endless luck, will run out.

it's a statistical inevitability.

[raven-at-the-writing-desk]

Ms Raven! I have a question about the research you're doing on the type of people and the relationship of their interest with the twst OB. Is there a quota of answerees that you try to meet or do you have a deadline for when you plan to close the survey? I'm just curious if there's a specific time frame we can count on in receiving the results of your study.

[Referencing this survey!]

As of my reply to this ask, we’ve already collected over 800 responses!! 🥳 It’s been less than a week since the survey form went public, so it’s been really exciting seeing this level of engagement and interest.

The survey form will still be accepting responses until July 15th. Ideally, we’d like to hit at least 1000 responses (which I think we’re well on our way towards). Even if we do meet our goal, we’ll happily accept as many responses as we can get before the deadline. BIG NUMBER GO BRRRRRRRRRR and also a larger sample size makes our data stronger 💪

Between now and July 15, we’re throwing together a template and model that will crunch the numbers for us when the data is prepared. This is because the final report will NOT be just pie charts and bar graphs showing the OB boy rankings; we will actually be using various statistical tests to compare the variables while accounting for potential confounding factors.

Once the form closes, we’ll move into the next phase, which will involve doing a sweep to “clean up” the data (ie throw out invalid responses, translate open-ended questions that have been written in languages other than English, standardize short responses so they all read the same (ie America/U.S./USA/United States of America -> USA), etc.). The cleaned data is what will be plugged in for analysis.

When the analysis is done, we’ll be in the writing phase. The average Joe will not be able to understand what these numbers, percentages, graphs, and tests mean. The hope is to produce a final report that is divusee up into sections like a research paper but is also fun + easy to read and understand. We plan to include an introduction, our methodology (why did we choose this test and these questions, how did we clean the data, etc.), results, discussion, conclusions, contributor credits, and even what we could theoretically do better next time.

Altogether, this process could take at least a few weeks (and that’s not counting time for editing and rewrites). If all goes well and there aren’t any unexpected bumps in the road, we can anticipate the full report being out in late August, maybe September. No promises though!! There’s various people involved, a lot of moving parts to account for, and, of course, a lot of data to play around with. At the end of the day, we want to make sure we know what we’re talking about before we pass that information along to the rest of the fandom!

[alpaca-clouds]

We need to talk about AI

Okay, several people asked me to post about this, so I guess I am going to post about this. Or to say it differently: Hey, for once I am posting about the stuff I am actually doing for university. Woohoo!

Because here is the issue. We are kinda suffering a death of nuance right now, when it comes to the topic of AI.

I understand why this happening (basically everyone wanting to market anything is calling it AI even though it is often a thousand different things) but it is a problem.

So, let's talk about "AI", that isn't actually intelligent, what the term means right now, what it is, what it isn't, and why it is not always bad. I am trying to be short, alright?

So, right now when anyone says they are using AI they mean, that they are using a program that functions based on what computer nerds call "a neural network" through a process called "deep learning" or "machine learning" (yes, those terms mean slightly different things, but frankly, you really do not need to know the details).

Now, the theory for this has been around since the 1940s! The idea had always been to create calculation nodes that mirror the way neurons in the human brain work. That looks kinda like this:

Basically, there are input nodes, in which you put some data, those do some transformations that kinda depend on the kind of thing you want to train it for and in the end a number comes out, that the program than "remembers". I could explain the details, but your eyes would glaze over the same way everyone's eyes glaze over in this class I have on this on every Friday afternoon.

All you need to know: You put in some sort of data (that can be text, math, pictures, audio, whatever), the computer does magic math, and then it gets a number that has a meaning to it.

And we actually have been using this sinde the 80s in some way. If any Digimon fans are here: there is a reason the digital world in Digimon Tamers was created in Stanford in the 80s. This was studied there.

But if it was around so long, why am I hearing so much about it now?

This is a good question hypothetical reader. The very short answer is: some super-nerds found a way to make this work way, way better in 2012, and from that work (which was then called Deep Learning in Artifical Neural Networks, short ANN) we got basically everything that TechBros will not shut up about for the last like ten years. Including "AI".

Now, most things you think about when you hear "AI" is some form of generative AI. Usually it will use some form of a LLM, a Large Language Model to process text, and a method called Stable Diffusion to create visuals. (Tbh, I have no clue what method audio generation uses, as the only audio AI I have so far looked into was based on wolf howls.)

LLMs were like this big, big break through, because they actually appear to comprehend natural language. They don't, of coruse, as to them words and phrases are just stastical variables. Scientists call them also "stochastic parrots". But of course our dumb human brains love to anthropogice shit. So they go: "It makes human words. It gotta be human!"

It is a whole thing.

It does not understand or grasp language. But the mathematics behind it will basically create a statistical analysis of all the words and then create a likely answer.

What you have to understand however is, that LLMs and Stable Diffusion are just a a tiny, minority type of use cases for ANNs. Because research right now is starting to use ANNs for EVERYTHING. Some also partially using Stable Diffusion and LLMs, but not to take away people'S jobs.

Which is probably the place where I will share what I have been doing recently with AI.

The stuff I am doing with Neural Networks

The neat thing: if a Neural Network is Open Source, it is surprisingly easy to work with it. Last year when I started with this I was so intimidated, but frankly, I will confidently say now: As someone who has been working with computers for like more than 10 years, this is easier programming than most shit I did to organize data bases. So, during this last year I did three things with AI. One for a university research project, one for my work, and one because I find it interesting.

The university research project trained an AI to watch video live streams of our biology department's fish tanks, analyse the behavior of the fish and notify someone if a fish showed signs of being sick. We used an AI named "YOLO" for this, that is very good at analyzing pictures, though the base framework did not know anything about stuff that lived not on land. So we needed to teach it what a fish was, how to analyze videos (as the base framework only can look at single pictures) and then we needed to teach it how fish were supposed to behave. We still managed to get that whole thing working in about 5 months. So... Yeah. But nobody can watch hundreds of fish all the time, so without this, those fish will just die if something is wrong.

The second is for my work. For this I used a really old Neural Network Framework called tesseract. This was developed by Google ages ago. And I mean ages. This is one of those neural network based on 1980s research, simply doing OCR. OCR being "optical character recognition". Aka: if you give it a picture of writing, it can read that writing. My work has the issue, that we have tons and tons of old paper work that has been scanned and needs to be digitized into a database. But everyone who was hired to do this manually found this mindnumbing. Just imagine doing this all day: take a contract, look up certain data, fill it into a table, put the contract away, take the next contract and do the same. Thousands of contracts, 8 hours a day. Nobody wants to do that. Our company has been using another OCR software for this. But that one was super expensive. So I was asked if I could built something to do that. So I did. And this was so ridiculously easy, it took me three weeks. And it actually has a higher successrate than the expensive software before.

Lastly there is the one I am doing right now, and this one is a bit more complex. See: we have tons and tons of historical shit, that never has been translated. Be it papyri, stone tablets, letters, manuscripts, whatever. And right now I used tesseract which by now is open source to develop it further to allow it to read handwritten stuff and completely different letters than what it knows so far. I plan to hook it up, once it can reliably do the OCR, to a LLM to then translate those texts. Because here is the thing: these things have not been translated because there is just not enough people speaking those old languages. Which leads to people going like: "GASP! We found this super important document that actually shows things from the anceint world we wanted to know forever, and it was lying in our collection collecting dust for 90 years!" I am not the only person who has this idea, and yeah, I just hope maybe we can in the next few years get something going to help historians and archeologists to do their work.

Make no mistake: ANNs are saving lives right now

Here is the thing: ANNs are Deep Learning are saving lives right now. I really cannot stress enough how quickly this technology has become incredibly important in fields like biology and medicine to analyze data and predict outcomes in a way that a human just never would be capable of.

I saw a post yesterday saying "AI" can never be a part of Solarpunk. I heavily will disagree on that. Solarpunk for example would need the help of AI for a lot of stuff, as it can help us deal with ecological things, might be able to predict weather in ways we are not capable of, will help with medicine, with plants and so many other things.

ANNs are a good thing in general. And yes, they might also be used for some just fun things in general.

And for things that we may not need to know, but that would be fun to know. Like, I mentioned above: the only audio research I read through was based on wolf howls. Basically there is a group of researchers trying to understand wolves and they are using AI to analyze the howling and grunting and find patterns in there which humans are not capable of due ot human bias. So maybe AI will hlep us understand some animals at some point.

Heck, we saw so far, that some LLMs have been capable of on their on extrapolating from being taught one version of a language to just automatically understand another version of it. Like going from modern English to old English and such. Which is why some researchers wonder, if it might actually be able to understand languages that were never deciphered.

All of that is interesting and fascinating.

Again, the generative stuff is a very, very minute part of what AI is being used for.

Yeah, but WHAT ABOUT the generative stuff?

So, let's talk about the generative stuff. Because I kinda hate it, but I also understand that there is a big issue.

If you know me, you know how much I freaking love the creative industry. If I had more money, I would just throw it all at all those amazing creative people online. I mean, fuck! I adore y'all!

And I do think that basically art fully created by AI is lacking the human "heart" - or to phrase it more artistically: it is lacking the chemical inbalances that make a human human lol. Same goes for writing. After all, an AI is actually incapable of actually creating a complex plot and all of that. And even if we managed to train it to do it, I don't think it should.

AI saving lives = good.

AI doing the shit humans actually evolved to do = bad.

And I also think that people who just do the "AI Art/Writing" shit are lazy and need to just put in work to learn the skill. Meh.

However...

I do think that these forms of AI can have a place in the creative process. There are people creating works of art that use some assets created with genAI but still putting in hours and hours of work on their own. And given that collages are legal to create - I do not see how this is meaningfully different. If you can take someone else's artwork as part of a collage legally, you can also take some art created by AI trained on someone else's art legally for the collage.

And then there is also the thing... Look, right now there is a lot of crunch in a lot of creative industries, and a lot of the work is not the fun creative kind, but the annoying creative kind that nobody actually enjoys and still eats hours and hours before deadlines. Swen the Man (the Larian boss) spoke about that recently: how mocapping often created some artifacts where the computer stuff used to record it (which already is done partially by an algorithm) gets janky. So far this was cleaned up by humans, and it is shitty brain numbing work most people hate. You can train AI to do this.

And I am going to assume that in normal 2D animation there is also more than enough clean up steps and such that nobody actually likes to do and that can just help to prevent crunch. Same goes for like those overworked souls doing movie VFX, who have worked 80 hour weeks for the last 5 years. In movie VFX we just do not have enough workers. This is a fact. So, yeah, if we can help those people out: great.

If this is all directed by a human vision and just helping out to make certain processes easier? It is fine.

However, something that is just 100% AI? That is dumb and sucks. And it sucks even more that people's fanart, fanfics, and also commercial work online got stolen for it.

And yet... Yeah, I am sorry, I am afraid I have to join the camp of: "I am afraid criminalizing taking the training data is a really bad idea." Because yeah... It is fucking shitty how Facebook, Microsoft, Google, OpenAI and whatever are using this stolen data to create programs to make themselves richer and what not, while not even making their models open source. BUT... If we outlawed it, the only people being capable of even creating such algorithms that absolutely can help in some processes would be big media corporations that already own a ton of data for training (so basically Disney, Warner and Universal) who would then get a monopoly. And that would actually be a bad thing. So, like... both variations suck. There is no good solution, I am afraid.

And mind you, Disney, Warner, and Universal would still not pay their artists for it. lol

However, that does not mean, you should not bully the companies who are using this stolen data right now without making their models open source! And also please, please bully Hasbro and Riot and whoever for using AI Art in their merchandise. Bully them hard. They have a lot of money and they deserve to be bullied!

But yeah. Generally speaking: Please, please, as I will always say... inform yourself on these topics. Do not hate on stuff without understanding what it actually is. Most topics in life are nuanced. Not all. But many.

[chosaraki]

Analytical mind.

—————————————————————————

Eugene x R.femele. ( Intense and psychological )

—————————————————————————

Y/N inspired by L Lawliet

—————————————————————————

Eugene was on his back, watching the flow of data on the floating screens. Lines of code pulsated slowly, like a living organism. In the background, light steps - barefoot - approached in silence.

- "You underestimated Daniel, again." - said a low, firm, emotionless voice.

He didn't turn around. He smiled, as always.

— "(Y/n), you know that unforeseen events are part of the game. The pieces react."

- "If you were playing chess... you would have already lost the king. For the third time." - she replied, huddled on the chair, with her knees touching her chin. The strange way of sitting resembled a spider preparing to jump.

She looked at him with dark, half-closed eyes. There was no accusation there - just a raw, logical analysis.

- "Funny..." - Eugene replied, approaching with a glass of wine that didn't even touch his lips. - "You seem angry, but your sugar has dropped. Didn't you eat anything today?"

She turned her face slowly. On the table, a small plate with chocolate-covered strawberries was intact. He himself had left it there before starting the day.

- "I'm waiting for the glucose of your next lie," she said, taking a strawberry with two fingers and biting like someone who analyzes a crime scene.

Eugene let out a slight laugh. His gentle smile was a mask that almost no one saw fall. But with her... there was no need for disguise. She saw everything. I calculated everything. Like a shadow I thought.

- "You still trust me, don't you?" - he asks, now sitting facing her. His golden eyes seemed to glow even in the dark.

She tilted her head slightly. The silence lasted exactly five seconds.

— "Trust is a statistical construction. Its average fell 12.3% in the last three reports."

- "Reports?"

- "You say while sleeping. And I listen." - she said with the same calm as always.

Eugene couldn't help it. She was the only person who made him... nervous.

——————

Flashback –

They had met during an illegal artificial intelligence auction in Seoul. She appeared without warning, wearing a T-shirt two larger numbers, bare feet and a box of supermarket candy. In the midst of billionaires and secret agents, she defeated everyone in a deduction game.

- "Your code has a primary error: you still think like a human." - she had told Eugene, when knocking down her AI with a single command line.

He's been obsessed ever since. But not with her. With the fact that she was the only variable out of his control.

———————

— Back to the present

- "Do you think I'm losing?" - Eugene asks, for the first time without smiling.

She blinks slowly. She takes a spoon out of an invisible pocket and dips it into the ice cream that Eugene didn't even realize she took from the fridge.

- "I think you're surrounded... and want to use me as your last way out."

- "And if I am?"

- "So... you have 48 hours. After that, or we win together..." - she licks the spoon, without emotion - "...or I'll knock you down first."

Silence.

Eugene stared at her reflection on the monitor. Small, almost fragile. But the look... that look.

- "You're the only person in this game that I don't know how to move."

She got up with a sloppy movement, her messy hair hiding part of her face.

- "Because I'm the whole board."

And she left the room.

———————

— Eugene's private room - high of the Workers' penthouse

Time: 02h47 —

Eugene was lying on his side, still dressed in the half-open dress shirt. The dark strands of hair stue to his forehead with the sweat that he hated to admit that it came from nervousness. He didn't sleep well when she was there.

She, as always, sat on the mattress with her knees close to her chest, bare back revealed by his shirt, which she wore as an improvised nightgown. The way he sat made the sheet messy, as if the bed was just a neutral point between two mental worlds.

- "Are you going to turn off your brain or are you still trying to predict tomorrow?" - she asks, her voice down, pulling a strand of his hair as if it were a loose thread in a program code.

He smiles, but the smile doesn't reach his eyes.

- "You're too close for me to think about tomorrow."

- "Lear." - she bites a candy gum. - "Her left pupil dilates whenever her dopamine rises, and she didn't dilate. You're thinking about Daniel."

Silence. Just his breath, deeper now.

- "I can't compete with you." - he admits. A weak whisper, as if something inside him surrendered. - "Not even here."

- "And I don't try to compete with you. I just... observe. Because you lie even when you sleep."

She crawls slowly until she lies down next to him, her cold and thin leg sliding over his. A strange gesture, disconnected, but that was intentional.

- "You lie to everyone. To the world. Pro Workers. For your brother."

She now looks directly into his eyes.

- "...But not for me. With me you just be silent. And that's more sincere than any of your words."

Eugene closes his eyes. For a moment, he feels peace. As if that mind - which could destroy him in a second - was the only one who understood the internal chaos that he tries to keep under control.

He moves his hand to her neck. A light touch, as if she wanted to check if she was still real. She doesn't react, she just turns her face and bites her index finger - with the delicacy of a bored cat.

- "Is that affection?" - he asks.

- "This is my way of demonstrating that you still have statistical value."

And they both laugh. For the first time, sincere laugh.

—————————————————————————

This (y/n) is the kind of woman who doesn't scream, doesn't exalt herself, and still dominates any environment. She doesn't love with traditional romanticism - but with logic, presence and ruthless analysis. Eugene loves her because she even sees what he hides from himself. But this love is a dangerous game. Both are addicted to control.

She doesn't kiss me like the others. Don't whisper sweet words. Don't undress for pleasure or bow for dominance.

But she enters my mind like no one else. Reorganizes my priorities without asking. And, ironically, it is the only human being that makes me vulnerable - not out of weakness, but out of transparency.

She's not a piece. She is the impossible variable. And maybe... the end of the game.

—————————————————————————

[werewolf-biter]

Vexen Character Analysis Week Day 4-Weapon Names

For this one I wanted to look at the names of his shields in Days. His shields are named for thing relating to either the cold, science and for a few of them both. I’ll look at both the English and Japanese names as some of them have different names depending on the language. I’ll also look at the name of his personal accessory.

Tester Zero- Considering all that came up when I looked this up was a 70s mecha anime I’m going to say this name was picked just to sound vaguely science-y while also making it clear this is his basic weapon.

Product One- Same with this one as well. It’s the shield he gets from the first gear you get so it has a name which fits with that role.

Deep Freeze/Refrigerator - Both of these are names that relate to the cold. I always find it so funny that one of his weapons is named refrigerator of all things.

Cryolite Shield- Cryolite is a rare mostly white mineral that is mainly found in Greenland. Cryo is also a prefix that means cold. This one uses the cold theming in a really neat way.

False Theory- This one refers to the idea of falsifiability, which deals with how a theory should be able to be proven false. It also may refer to the idea of a null hypothesis or the idea that a hypothesis isn’t true due to there being no statistical significance. This name is interesting as it conveys the ideas of a theory that has been proven wrong, which draws some parallels with the research done by the apprentices.

Glacier- Glaciers are massive dense body of ice that slowly move and create landforms in their wake. Another one that’s just meant to keep with the cold theme.

Absolute Zero- zero Kelvin is the coldest possible temperature . At that point all motion stops and only a small amount of energy is left. A fitting name for a weapon wielded by a cryokinetic scientist.

Gunz-This shield takes its name from a glacial period. There’s another two that continue this theme.

Mindel- Another glacial period one. Interestingly all the glacial periods referenced are also the names of tributaries of the Danube.

Snowslide- This name evokes images of rushing avalanches and fierce blizzards. This suits his fighting style in CoM pretty well, less so in KH2 and Days though.

Iceberg- an iceberg is a large chunk of ice floating in a body of water. They also tend to be connected to the idea of things existing under the surface. I would imagine it was picked for cold theming, but it also works in the context of darkness existing within the realm of light.

Inquisition/Researcher- This two are both science related terms. Researcher is a word that generally means the same thing as scientist. Inquisition can mean a few things, but in this case it is referring to scientific curiosity. Unchecked curiosity was what started Even fall to darkness, which makes this a particularly interesting choice of name.

Scrutiny/Experiment- An experiment is a research method used to establish causation between independent and dependent variables. Scrutiny is part of the scientific norm of organized skepticism or the idea that research should be properly analyzed before being accepted. This is the reason that studies are peer-reviewed.

Empiricism/Closed Tool- So I don’t know what a closed tool is and all looking it brought me to was the idea of closed source. I don’t think that’s what they were going for, but it does work nicely with the secrecy that the Organization and its members tends to practice Empiricism refers to the empirical method, the standard that scientific research holds it self as a means of establishing validity in its results.

Edification/Laboratory -Laboratories are places where research is conducted. Edification is the act of trying to improve one’s self intellectually, morally or spiritually. Like with inquisition this can connected with his fall to darkness, which saw him perusing knowledge without little regard for the hurt it may cause.

Contrivance - In this context this is likely referring to a finding that came down to confounding factors instead of what was being researched. It can also refer to data being manipulated to get a wanted result. From what has been seen, it seems like quite a bit of the research down by both the apprentices and the Organization was of this nature.

Würm- This is the last of the shields named for glacial periods. Glacial periods are intervals of colder weather during ice ages.

Subzero- A temperature below 0. Analysis one that was done to fit the ice theming.

Cold Blood- A phrase used to describe someone who does cruel acts in a callous and detached manner. Interestingly this doesn’t actually fit Vexen all that well, as he is ironically quite hot-blooded, cold-blooded is more a term I’d use to describe characters like Xemnas, Marluxia and Saïx. Likely picked for both cold theming and to give the image of someone conducting unethical research in a clinical way.

Diamond Shield-This comes from the meteorological phenomenon know as diamond dust which sees the creation of clouds of tiny ice crystals. Square has used this as the name for a variety of things, most famously Shiva’s signature attack, but also as the name for one of Vexen’s sleights and his Limit Break in Days.

Aegis- An aegis is an object of protection. The term comes from Greek mythology we’re it was used for a divinely blessed object that has depicted as a cloak, a shield or a breastplate. This one is unique as it doesn’t follow the theming of the rest of his weapons and instead comes from the weapon he wields. Protection in relation to him goes beyond just his shield, as Even was Ienzo’s main guardian, a job he seemed to take to heart as a Somebody.

Frozen Pride- This name actually predates Days as it came from KH2FM. This is why it doesn’t completely follow the naming scheme. The name comes from a combination of Vexen element and his extremely haughty nature. It may also reflect how despite his pride, he doesn’t really get that much respect from some of the other Org members.

Pot Lid- It’s a pot lid and was likely picked as a silly type of shield as this is a gag weapon.

Snowman- The other gag weapons. It’s a small shield with a little snowman on it. What’s interesting is that its description says it reflects the user personality, which leads to the question of what the weapon could mean. Maybe it refers to him creating Replicas, maybe it’s just because he controls ice, or maybe it’s meant to harken back to happy winter memories as a Somebody, who knows.

Ice Breaker/Smiles of Ice and Snow This is his personal accessory that gives complete resistance to being frozen. The English name is just a quip reflecting his ice powers, but the Japanese name is far more interesting. It fascinates me because it has seemingly happy connotations that evoke the image of a pleasant cozy winter. If it had been Smile of Ice it would have given the image of cold sadistic glee, but the addition of snow gives a sweeter softer feeling. Like Aegis and Snowman it seems to harken back to his role as Ienzo’s guardian.

Looking at these as a whole there are some things to note. Most of the ice related one seem to have been picked mainly for the purpose of theming and do not give a lot to work with. The science related ones on the other hand show something of a pattern, particularly with the English names. Most of them are connected to curiosity and the concept of falsifiability. Their names seem to come from the qualities that lead to him becoming a Nobody, a ravenous urge to find more knowledge and less than stellar research methods. They reflect has absolute worst qualities as a researcher. In contrast to that, the ones the connected to protection reflect has best quality when he was a Somebody, the care he once had for others. It leads to the names evoking of feeling of things that have been lost and sins of the past.

Day 1 <3 Day 2 <3 Day 3 <3 Day 5 <3 Day 6 <3

Day 7 <3 Bonus

[jessicamessicavillageofspillage]

Apparently transpeople will also die from the inaccurate recording of Sex within statistics

The collection of data on a person’s sex – that is, whether they are male or female – has become controversial in recent years, and a number of public bodies have moved away from collecting data on sex as a result. For example, Scotland’s chief statistician recently issued guidance stating that data on sex should only be collected in exceptional circumstances. This move has been greeted with alarm by quantitative social scientists who believe that data on sex is vitally important and that data on both gender identity and sex is needed.

The Office for National Statistics (ONS) was also embroiled in controversy when it proposed to guide respondents to the 2021 England and Wales census that they may answer the sex question in terms of their subjective gender identity, rather than their sex. This was despite the fact that the 2021 census also included a new separate question on gender identity. The ONS was forced to change its proposed guidance on the sex question by a judicial review and went on to advise that people should answer the first question to reflect their legal sex. The Scottish census authorities have been criticised for disregarding the implications of that judgment.

Statistics on employment, health, crime and education have all been affected by this trend.

The Government Equalities Office has issued guidance to employers who are legally bound to report on their gender pay gap to provide data on their employees’ gender identity, not their sex, and to exclude employees who “do not identify as ‘men’ or ‘women’” from the data. This makes it impossible to assess whether natal males who identify as trans or non-binary may have different labour-market experiences from natal females who identify as trans or non-binary. Yet non-binary or transgender identification may not protect females from discrimination, for example, on the basis of pregnancy or maternity or the perceived risk of becoming pregnant.

The NHS decides who to call for routine medical screenings based on the gender marker a person has recorded with their GP rather than their sex as recorded as birth. The NHS’s failure to record biological sex on patient records has led to trans patients not being called in for screening for conditions that may affect them due to their sex, such as ovarian cancer or prostate cancer. If trans patients are not screened for such conditions, the consequences are potentially fatal. The use of gender identity rather than sex has also led to confusion for some trans patients attempting to use sexual health services.

Freedom of information requests have revealed that multiple police forces in England now record crimes by male suspects as committed by women if the perpetrator requests to be recorded as such. Even small numbers of cases misclassified in this way can lead to substantial bias in crime statistics.

Differences between the sexes are an important factor for analysis in most, if not all, of the areas that social and health scientists address. Sex, alongside age, is a fundamental demographic variable, vital for projections regarding fertility and life expectancy. Sex has systematic effects on physical health and is also linked to mental health. And the importance of sex extends to all aspects of social life, including employment, education and crime.

We know that many differences between the sexes have changed dramatically over time – education and labour market participation are two examples. Without consistent data on sex, social scientists would not be able to track this change over time or to understand whether efforts to improve the representation of women and girls in domains where they are underrepresented have been effective.

We have been losing data on sex, as public sector bodies have switched to collecting data on gender identity instead. But the tide may have turned. The UK Statistics Authority has recently published guidance that recommends that “sex, age and ethnic group should be routinely collected and reported in all administrative data and in-service process data, including statistics collected within health and care settings and by police, courts and prisons”. It also says data producers should clearly distinguish between concepts such as sex, gender and gender identity.

Both people’s material circumstances and their identities are important to their lives. We know that sex matters, and we have much to learn about the ways in which gender identity matters, too. Rather than removing data on sex, we should collect data on both sex and gender identity, in order to develop a better understanding of the influence of both of these factors and the intersection between them.

Original article in The Conversation

Professor Alice Sullivan’s academic profile

UCL Social Research Institute