#Inferential statistics

[marketxcel]

5 Methods of Data Collection for Quantitative Research

Discover five powerful techniques for gathering quantitative data in research, essential for uncovering trends, patterns, and correlations. Explore proven methodologies that empower researchers to collect and analyze data effectively.

[virmire]

someone explain two way ANOVA test to me like I’m a toddler lol

[tedkaczynskiofficial]

They can probably accurately guess about how many people are LGBT+ by comparing how the large number of people who explicitly state it in their bio use the site to users who don't explicitly state their sexuality somewhere on their blog.

LGBT+ people probably make way more than 25% of the posts here, but that doesn't mean cis/het people who like/reblog aren't users of the website. % of interaction on the site doesn't necessarily describe the actual demographic breakdown of users (it's still interesting/useful information, though).

I'm also going to point out that staff might actually not be super biased in their handling of reports, but that if only posts by trans people get reported that trans people are disproportionately going to be affected by their moderation. I'm sure there is some level of bias involved by the staff themselves, but seeing as we know TERFs and whatnot intentionally report trans people, it really would make the most sense for that to be where the majority of the disparate impact comes from.

Thanks for all of the recent feedback around Community Labels being incorrectly applied to content. In particular, we appreciate the input we’ve received from the LGBTQIA+ community and understand the frustrations from folks who felt that their content was unfairly labeled. When we realized this was happening, we immediately investigated and are taking steps to prevent this from happening again.

The LGBTQIA+ community makes up about a quarter of the Tumblr community. It is important for us to support all Tumblr users, especially those whose safe spaces are under threat in certain parts of the world.

As you know, alongside of the rollout of Community Labels we also expanded the types of content allowed on Tumblr as a way to welcome more creativity, art, and self-expression. Our goals remain the same today. Human error happens and we apologize to anyone who has been impacted by these mistakes.

We are working to better understand what happened and will follow up with more information soon.

[mitcenter]

Descriptive vs Inferential Statistics: What Sets Them Apart?

Statistics is a critical field in data science and research, offering tools and methodologies for understanding data. Two primary branches of statistics are descriptive and inferential statistics, each serving unique purposes in data analysis. Understanding the differences between these two branches "descriptive vs inferential statistics" is essential for accurately interpreting and presenting data.

Descriptive Statistics: Summarizing Data

Descriptive statistics focuses on summarizing and describing the features of a dataset. This branch of statistics provides a way to present data in a manageable and informative manner, making it easier to understand and interpret.

Measures of Central Tendency: Descriptive statistics include measures like the mean (average), median (middle value), and mode (most frequent value), which provide insights into the central point around which data values cluster.

Measures of Dispersion: It also includes measures of variability or dispersion, such as the range, variance, and standard deviation. These metrics indicate the spread or dispersion of data points in a dataset, helping to understand the consistency or variability of the data.

Data Visualization: Descriptive statistics often utilize graphical representations like histograms, bar charts, pie charts, and box plots to visually summarize data. These visual tools can reveal patterns, trends, and distributions that might not be apparent from numerical summaries alone.

The primary goal of descriptive statistics is to provide a clear and concise summary of the data at hand. It does not, however, make predictions or infer conclusions beyond the dataset itself.

Inferential Statistics: Making Predictions and Generalizations

While descriptive statistics focus on summarizing data, inferential statistics go a step further by making predictions and generalizations about a population based on a sample of data. This branch of statistics is essential when it is impractical or impossible to collect data from an entire population.

Sampling and Estimation: Inferential statistics rely heavily on sampling techniques. A sample is a subset of a population, selected in a way that it represents the entire population. Estimation methods, such as point estimation and interval estimation, are used to infer population parameters (like the population mean or proportion) based on sample data.

Hypothesis Testing: This is a key component of inferential statistics. It involves making a claim or hypothesis about a population parameter and then using sample data to test the validity of that claim. Common tests include t-tests, chi-square tests, and ANOVA. The results of these tests help determine whether there is enough evidence to support or reject the hypothesis.

Confidence Intervals: Inferential statistics also involve calculating confidence intervals, which provide a range of values within which a population parameter is likely to lie. This range, along with a confidence level (usually 95% or 99%), indicates the degree of uncertainty associated with the estimate.

Regression Analysis and Correlation: These techniques are used to explore relationships between variables and make predictions. For example, regression analysis can help predict the value of a dependent variable based on one or more independent variables.

Key Differences and Applications

The primary difference between descriptive and inferential statistics lies in their objectives. Descriptive statistics aim to describe and summarize the data, providing a snapshot of the dataset's characteristics. Inferential statistics, on the other hand, aim to make inferences and predictions about a larger population based on a sample of data.

In practice, descriptive statistics are often used in the initial stages of data analysis to get a sense of the data's structure and key features. Inferential statistics come into play when researchers or analysts want to draw conclusions that extend beyond the immediate dataset, such as predicting trends, making decisions, or testing hypotheses.

In conclusion, both descriptive and inferential statistics are crucial for data analysis and statistical analysis, each serving distinct roles. Descriptive statistics provide the foundation by summarizing data, while inferential statistics allow for broader generalizations and predictions. Together, they offer a comprehensive toolkit for understanding and making decisions based on data.

[thatstoomanysausages]

My psychology classmates should be thanking me on their knees for lowering the curve fr💪

[statswork]

Expert Statistical Consulting and Data Analytics Solutions

Your data analytics and statistical solutions partner. Unleash data's potential for research, academia, and business success with our expertise

[easternindiatechnosoft-blog]

Big Data Analysis Company in Kolkata

Introduction

In the dynamic landscape of technology, big data has emerged as a game-changer for businesses worldwide. As organizations in Kolkata increasingly recognize the importance of harnessing data for strategic decision-making, the role of big data analysis companies has become pivotal.

The Rise of Big Data in Kolkata

Kolkata, known for its rich cultural heritage, is also witnessing remarkable growth in the realm of big data. Over the years, the city has transitioned from traditional methods to advanced data analytics, keeping pace with global trends.

Key Players in Kolkata’s Big Data Scene

Prominent among the contributors to this transformation are the leading big data analysis companies in Kolkata. Companies like DataSolve and AnalytixPro have carved a niche for themselves, offering cutting-edge solutions to businesses across various sectors.

Services Offered by Big Data Companies

These companies provide a range of services, including data analytics solutions, machine learning applications, and customized big data solutions tailored to meet the unique needs of their clients.

Impact on Business Decision-Making

The impact of big data on business decision-making cannot be overstated. By analyzing vast datasets, companies can gain valuable insights that inform strategic decisions, leading to increased efficiency and competitiveness.

Challenges and Solutions

However, the journey toward effective big data implementation is not without challenges. Big data companies in Kolkata face issues like data security and integration complexities. Innovative solutions, such as advanced encryption algorithms and seamless integration platforms, are being developed to address these challenges.

Future Prospects

Looking ahead, the future of big data in Kolkata appears promising. The integration of artificial intelligence and the Internet of Things is expected to open new avenues for data analysis, presenting exciting possibilities for businesses in the city.

Case Study: Successful Big Data Implementation

A closer look at a successful big data implementation in Kolkata reveals how a major e-commerce player utilized data analytics to enhance customer experience and optimize supply chain management.

Training and Skill Development

To keep up with the evolving landscape, there is a growing emphasis on training and skill development in the big data industry. Institutes in Kolkata offer comprehensive programs to equip professionals with the necessary skills.

Big Data and Small Businesses

Contrary to popular belief, big data is not exclusive to large enterprises. Big data companies in Kolkata are tailoring their services to suit the needs of small businesses, making data analytics accessible and affordable.

Ethical Considerations in Big Data

As the volume of data being processed increases, ethical considerations become paramount. Big data companies in Kolkata are taking steps to ensure data privacy and uphold ethical standards in their practices.

Expert Insights

Leading experts in the big data industry in Kolkata share their insights on current trends and future developments. Their perspectives shed light on the evolving nature of the industry.

Success Stories

Success stories from businesses in Kolkata highlight the transformative power of big data. From healthcare to finance, these stories underscore the positive impact that data analysis can have on diverse sectors.

Tips for Choosing a Big Data Analysis Company

For businesses considering a partnership with a big data company, careful consideration of factors such as experience, scalability, and data security is crucial. Avoiding common pitfalls in the selection process is key to a successful partnership.

Conclusion

In conclusion, the journey of big data analysis company in Kolkata reflects a broader global trend. As businesses increasingly recognize the value of data, the role of big data analysis companies becomes indispensable. The future promises even greater advancements, making it an exciting time for both businesses and big data professionals in Kolkata.

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[hyunebunx]

˖˙ ᰋ ── i didn't hear what you said, i just want to kiss you

﹙ʚɞ˚﹚. genre: fluff

﹙ʚɞ˚﹚. a/n: this is for all of my perfectionist students lmao. kind of self indulgent and super inspired by hyunjin's latest live. enjoy!! <3<3<3

For years now, your boyfriend has been your favorite study partner. Always patient, kind, and considerate of your needs, helping you tackle every difficult subject with a smile on his face. Bringing you snacks, urging you to take breaks whenever he sensed you needed it but most importantly, never pressuring you in any way. Despite your stellar marks, he always says:

“Don’t stress too much about it. Grades aren’t everything.”

And you believe him, you really do, yet the overachiever part of your soul is always louder, and never lets you rest, yelling in your ear until you comply and spend your whole day cooped up inside, studying.

You need to get the highest grade possible, otherwise you’ll shrivel up and die.

Hyunjin keeps you grounded, that’s why there’s no better person alive than your boyfriend. An angel in disguise who has somehow fallen from grace, lost his wings, and is now trapped on earth, forced to mingle with mere mortals like you.

And mingle he does. But unfortunately for him that’s not enough – he also has to teach you statistics.

“See? The difference between descriptive statistics and inferential statistics is quite simple. It’s easier to tell them apart now, right?”

“I guess…” You yawn, setting your glittery pen aside before stretching your arms above your head. “I need a break.”

Hyunjin cocks a brow, amused. “We just started.”

“Half an hour ago!” You point towards the clock on the far wall, hidden behind endless amounts of bookshelves.

“Exactly, we barely managed to scratch the surface.” He pouts, running a hand over his buzzed head in slight exasperation.

You have to resist the urge of squishing his cheeks together, not wanting to make a scene in public. Cuteness aggression was a real thing you fought with every day. “I’m not going to lie, my love. I stopped listening to whatever you were explaining 15 minutes ago.”

“What?”

You nod. “I didn’t hear anything you said.” Then, you scoot closer, gluing yourself to his side as your voice drops several octaves. “I just want to kiss you.”

Hyunjin’s eyes widen slightly at your confession, swiftly looking around to ensure the nearby tables are still vacant. Then, he tongues his cheek in the most attractive way you’ve witnessed, a smirk hanging off the corners of his mouth as he shakes his head.

“After you finish this chapter.” He eventually breathes out, allowing one of his hands to rest on your upper thigh and squeeze in encouragement.

Your head falls back with a groan, frustrated. “Come on, Hyun!” the way you drag out his name has him chuckling lowly, eyes sparkling. “Haven’t I suffered enough?”

“Suffer?” He laughs, poking your forehead. “You’ll only suffer if you fail this test.”

“I won’t fail.” You huff, jerking back. His hand then slips off your thigh and the lack of warmth has you scrawling right back, wounding your arms around his neck to bring him even closer, hoping he’ll cave.

Hyunjin’s eyes fall to your lips, and you know it’s a matter of time before the spell you got him under works its magic. “Of course, you won’t. I won’t allow it.”

Your bright smile snaps his attention back to your eyes, which he seems to get lost exploring, absorbed by the beautiful color. Without missing a beat, you lean forward to connect your lips, eager to taste the cherry chapstick you applied on him when he complained about his lips being dry.

You guess even angels can get dehydrated.

Making out at the library on a Thursday night was never on your bingo card, but with Hyunjin as your partner in crime, you wouldn’t mind doing anything. He makes you feel safe in any situation, but especially when you have to get out of your comfort zone, tackle life head-on when putting things on hold is no longer an option.

You manage to peck his lips, once, twice, and then three times before he brings you closer, big hands sliding down from your waist to your hips and squeezing, needing to feel your flesh between his fingers.

His tongue brushes against your lower lip, and as your mouth opens to allow him access to every part of you, a low moan escapes you both simultaneously. Alcohol was overrated – you’ve only ever gotten drunk on each other.

“We don’t even share a major.” He gasps as he pulls away, and your lips find his jaw.

“I know.” Another kiss graces the beautiful mole under his eye.

With the way you’re kissing him, your lips trailing down his throat, Hyunjin has trouble speaking. “I-I’ve never taken this class before.”

“I know.” You nod, pecking the base of his neck.

A shiver runs down his spine, and his hold on you tightens, almost like he’s ready to lift and place you on his lap, deeming you too far away. “So why do I keep helping you like I’m some dean’s list student?”

“Because you love me.” You finally stop to look into his eyes, heart fluttering at the way his chest is already weaving up and down after a few minutes of innocent kisses. Your touch has always had that effect on him, so you were never confused about his feelings towards you. Hyunjin wore his heart on his sleeve, body reacting faster than his brain could process, never failing to show you how near and dear you are to him. How much he adored every one of your endearing quirks, loving you unconditionally like it was a duty he never wanted to be free of. “As much as I love you.”

With a cocky smirk he barely manages to muster, he replies while tucking some hair behind your ear. “I think I love you a little more than you love me, actually.”

“That’s impossible, Hyun.”

And you were certain of it. Nothing could be bigger than the love you held for this angel.

[valentinedrifter]

Snippets with Ningning: Pink

Ningning x Eunha

~2.8k words

A/N: Prompt by @woollypoison, Thanks for hosting, much love!

Enjoy.

Yizhuo doesn’t know why the fuck you’re dating that stupid bitch.

Like, seriously? Out of everyone, you’re in bed with her? The fucking pink-haired bitch with the most kissable Goddamn lips, thighs that could pass off as fucking earmuffs, and tits she could just squeeze like lemo-

Okay, so maybe she sees what you see in the bitch, but that doesn’t mean she has to like it. And what the hell does the slut have that she doesn’t?

She’s got a pretty good pair of lips that she knows could take your soul away if she ever got the chance to go down on you—nine out of ten recommended—and while her tits aren’t as big as the bitch has it, Yizhuo still has quite the set that can most definitely wow you when you get a hold of them. Oh, and her ass, her fucking ass can honest to God choke you out if she ever decides to sit on your face.

Shit, she had pink hair too for like, two months, so why didn’t you try anything with her? 

If she tried hard enough, she can be the cover girl for some fashion brand out there. She has class. Standards. Self-respect, dignity if she wants to push it, not like the bitch that everyone wants to bend over their desk.

Yizhuo’s smarter than the stupid idiot that can’t even do inferential statistics to save her life. She gets As on average, and she can talk your ass off about anything that wasn’t just about getting fucked on the daily.

She helped you understand what derivatives and limits are for calculus. And where was Barbie from Temu? Getting railed in the clinic, that’s where the hell she was.

Like, damn, she can cook real food. Not the instant noodle bullshit at the local convenience store or the quick sandwich that doesn’t even count. Yizhuo can cook the good shit. Hot pots, grilled pork, she can make salmon if you were into that. Food that’s made with love. Food you damn well deserve.

So what in the fuck is she missing?

Did she need to go back to dying her hair pink just so you can notice her? Did you like bigger tits? A fatter ass? Did Yizhuo need to make you lunch every damn day?

Was it because the free prostitute won the genetic lottery, because damn if the slut didn’t need makeup to look that fucking hot. 

It was bullshit. She should be the one bragging all over campus, not the dumb bitch that stole you under her nose. Stupid whore doesn’t even treat you right, because if that wasn’t enough, she’s also a toxic piece of shit.

Yizhuo knows the rumors. About how the slut sleeps with practically everyone, from the math nerd, the volleyball star, the history professor, the fucking janitor. The campus mascot even got lucky, while wearing the fucking suit. She doesn’t know how the logistics of that would even work.

Yizhuo heard from Lia that a teacher caught Pinky and the Dean with the door open. Not closed, not locked. Open. Judging from the fact that nothing happened, she probably slept with the teacher too.

There’s even that one time where the dumbass set off the fire alarm in the middle of a quickie. How the hell does that even happen?

Speaking of alarms, Pinky’s a walking red flag, a red alert, a tactical nuke type of danger that screams typhoon siren sounds out of her ass, and she wears it like a medal. Why she’s proud of it, Yizhuo will never know. She gives props for confidence though.

And don’t even get Yizhuo started on all the exes that the bitch got bored of, or cheated on, or destroyed a perfectly happy relationship for a quick fling. Bitch is playing eenie-meenie-miney-mo at this point with how high her body count is. She’s a certified cum dumpster that’s free Twenty-Four-Seven.

She’s surprised that the slut hasn’t gotten a disease from the amount of people that’s gotten in and out of her. 

You know all about it when she asked—totally not because she isn’t curious as to why you would try and date the walking condom—and all you had to say was-

“I don’t think she did all that.”

What the hell do you mean you don’t believe them, Yizhuo thinks, because everyone and their mother knows about what the hell the tramp’s done. Shit, the motherfucker has most likely fucked a mother too, if the rumor about her and the librarian was true; It probably is.

Was that it? Were you into bad bitches? Did you have that ‘I can fix her’ kink that always went wrong because this isn’t some movie that gives you those silly happy endings. 

Then again, you were optimistic like that. So innocent, so sweet, Yizhuo could just pinch your cheeks because of how cute you are-

Hold on, does she need to do that too? Start wearing tight tops, start fucking everyone she sees in a five meter radius, holy fuck does she need to fuck the janitor? 

She sure as shit wasn’t petty about it. Nope. Nada. No ma’am. She just doesn’t understand why you would look at someone like Pinky and not like her. 

She’s been with you throughout everything, the highs and the lows, the in-betweens, the break ups—which, your relationship with that bitch will definitely end up on—yet, you don’t even see Yizhuo as something more.

She’s trying to be supportive about it like she always did, but that whore is really making it hard for her to root for the both of you. But as your best friend, your confidant, she would endure.

But if she sees you with that bitch one more damn time, she’s getting a flamer somewhere—she’ll make one herself if she has too—and turn this campus into a fire hazard.

Truth be told, it needs the cleansing after everything the human fleshlight has done on every surface imaginable. Desks, doors, public benches. She probably needs to burn the statue in the middle of the main hall too.

Okay, so maybe Yizhuo’s going off the deep end, but she swears that this is an extremely reasonable crashout, cause at this point, the campus wants to be burned. After everything its witnessed, she can consider it consensual arson, and she’s just there to get it started. 

It would be so easy too. That Gauel chick from chemistry made some sort of homemade project last year, and she could probably make a copy-

“Hey!”

The shout made her snap her head so fast she got whiplash. Her mind’s still mentally noting all the things she needs before it registers who called her.

You. Standing there, all cute, that cheeky smile filling your face that makes her want to squeeze your face out because of how adorable you are. 

Yizhuo has to dig her nails into her notebook to stop herself from just grabbing you and shoving her tongue down your throat.

And you don’t even know that you’re using that smile as a weapon because damn does that make her filthiest fantasies overwrite everything that she was thinking of from the last ten minutes. Shit, that smile’s enough to get her in the mood when her thighs unconsciously press together.

It would be so damn easy to just, like, take you right here, in the library where anyone can hear and everyone can look. Yizhuo sees the vision forming inside of her mind. 

The way you’d wrap your lips around her pretty little fingers, throating two, no, three of them down and you’d fucking take it like the throat GOAT she imagines you are.

Then she would fuck your mouth with them while you’re on your knees, and you’d have your hands on her thighs, tears and spit spilling down your chest, messing up that snug little t-shirt you’re wearing.

God, Yizhou would suck the life out of you. First with your mouth after it's been thoroughly used by her fingers. She’d explore every single inch of that mouth, and she’d get sloppy with it too. Nip at your plump fucking lips, lick the spit that’s dripping down your chin. 

She’s getting wet at the thought of you moaning out her name.

She’d bend you over the table and spank that absolute dump truck of an ass you’ve got. Yizhuo wonders how much that juicy flesh would ripple every time she’d give each cheek a hard slap. 

She would even get a handful of it, and she’d burn the feeling of that big, fat ass into her memory if she could.

She’d yank those jeans down your legs, give you another hard slap on that bare ass, and she’d go to town on you. But she’d go slow. Use her hands to get you all worked up, make you beg for her to use her pretty little mouth. And when she does, Yizhuo’s gonna savour the look on your face-

Wait. Since when did you have pink hair?

That threw her out of her daydreams, because last she checked, you had blonde hair. Now suddenly it’s this light pink that’s oddly similar to the slut you’re dating.

You’re still looking at her. Blinking, smiling, like you don’t have a fucking clue what was going on in Yizhuo’s mind, full of intrusive thoughts and debauchery all because of two completely different women.

“Eunha!” Yizhuo tucks a strand of hair back, giving you—her—a timid smile. “I…thought you had class.”

Jung Eunbi. Eunha, to those who know her. Yizhuo’s best friend. Also known as the love of her life.

“The prof got sick, so I got some time to kill.” Eunha plops down the chair in front and crosses her arms. “And you have been avoiding me.”

“No I haven’t.” Yizhuo lies, smooth as hell, cause she’s done this too many times in the past few weeks, fiddling with the pen on the desk that she was supposed to be using to write math equations. “Professor Roh’s been swarming us with work. I swear she’s at that time of the month.”

Eunha laughs, giving Yizhuo those tingles on her stomach that she seriously cannot be having right now. “Everyone’s swarming us with work. Even professor Myoui, and she barely gives anything out.”

For a while, it was normal again. Yizhuo and Eunha, messing around as always. No problems, no avoiding, no reminders of who Eunha was meeting at the end of the day.

Well, except for her pink hair which-

“When did you dye your hair?” Yizhuo pretends to be curious but she’s really just fishing cause she knows that Pinky’s involved in it somehow.

“Like a week ago.” Eunha’s twirling the ends of her curls, and fuck if Yizhuo really just wants to tell her that she really shouldn’t be doing that in front of her, because even though the color’s a stark reminder of the slut she’s dating, she looks even prettier with it.

And Yizhuo really shouldn’t be imagining the things that she wants to do to Eunha again.

“I would’ve asked my best friend,” Yizhuo can’t help but look to the side for that. “For help but she hasn’t been responding to my texts lately.”

“Your girlfriend might get angry.” That was the shittiest excuse she could’ve given, Yizhuo lets the stray thought cross through her mind, but she might as well commit to the bit. “I was trying to give you space.”

“She doesn’t care.” Eunha says, shaking her head, chuckling. “She knows that nothing’s going on between us. And she knows we’ve been friends for like, forever.”

It felt like Yizhuo got shot and left dead in a ditch somewhere when she heard those words. Nothing, Eunha says. Friends since forever, Eunha says. Yizhuo’s been trying to get something going but she keeps pussying out of it.

Her fault, really. She’s let so many chances slip by and now this happens. Eunha taken away from one of the worst people Yizhuo can imagine.

The bitch not caring really did sound like her, to be honest.

Yizhuo was about to say something along the lines of ‘Why she’s still with her’ again but she didn’t have to, because the stupid idiot decided to do it for her.

“Baby!”

And there she is. The Queen Bitch of the campus strutting into the library, dressed like a cheap whore. Boxy glasses that had no lens, ponytail held up to the side, the school girl outfit with the short skirt and the top that showed off how big her tits are. That same shade of pink coloring her hair, just a bit darker than Eunha’s.

Uchinaga motherfucking Aeri. Giselle, to those who know her. And everyone fucking knows her.

“Gigi!” Eunha stands up, giving Aeri—Yizhuo is not going to call her Giselle for fuck’s sake—a hug.

Aeri wraps an arm around Eunha’s waist like it was supposed to be there, like she’s done it so many times. And she has. Just not with Eunha.

Yizhuo did not feel her eye twitch. 

Not at goddamn all.

“Miss me already babe?” Aeri leaves a kiss on Eunha’s temple, and Yizhuo really hates how it’s making Eunha blush.

“Just a little bit.” Eunha lets out this shy giggle that makes Yizhuo want to bang her head on the desk. “I-uhm, I dyed my hair pink.”

“Looking like a snack.” Aeri pulls back, enough to get a good look at Eunha, who’s looking down on the ground, cheeks becoming rosy. “Pink suits you.”

Yizhuo’s resisting the urge to roll her eyes.

“I wanted to try something new.” Eunha replies, glancing up to Aeri, quick, hidden. That one little gesture was enough for Yizhuo to realize why Eunha dyed it.

She looks away, her own cheeks reddening from anger, shame, insanity. Were they seriously flirting in front of her? It’s like she wasn’t even there, and the fact that she feels replaced by Aeri is like a punch to the damn gut.

What she wouldn’t do to be in that bitch’s place.

And suddenly Yizhuo hears alarm bells go off.

At first, it was a glance. Aeri’s eyes move away from Eunha to her, then her entire head turns, and she hears those sirens go off louder in her head.

Because now Aeri’s eyeing her up like a snack, licking her lips, eyeing her from head to toe. It is seriously making her feel unsafe in the quiet working environment she calls her second home.

She is not thinking what Yizhuo thinks she’s doing right now. Hell no. She’s seeing things.

Aeri’s gaze stays on her, tilting her head, bedroom eyes landing on her chest. Yizhuo should’ve worn a jacket.

Please, do not let her be serious, Yizhuo is hoping, praying that any deity out there can answer her. She knows it’s useless, but it’s worth a try anyways.

“Hey, Yizhuo.” Aeri starts, lips tugging upwards, slow, predatory, unsafe. “Can I call you Ningning? Eunha always calls you that.”

No. “Sure, I guess.” Yizhuo knew that was a mistake pretending to be friends with this bitch because Aeri’s smile got wider. 

She sees Eunha smile too, leading her and Aeri to sit down on the table, completely oblivious to the fact that her best friend is being eye fucked by her girlfriend. “Found Ningning here studying for Professor Roh’s exam and figured we could catch up.”

“Is she now?” Aeri drawls, hand on her chin, still giving Yizhuo that fucking look. 

“Lots of things to do, you know.” Yizhuo replies, looking down at her notebook, really hoping that Aeri can fuck off. Her prayers were…not answered.

“You think she’d be down to help tutor us?” Aeri asks her girlfriend—that’s so gross to think about—but her eyes are staying with Yizhuo.

Oh fuck no, is what Yizhuo would love to answer, but Eunha, sweet, innocent Eunha, makes that response impossible. 

“That’s a great idea!” Eunha beams and nods at her, excited at the prospect.

“I know, right?” Aeri grins. “I think it’ll be very educational.”

No it will not, Yizhuo thinks, but the words don’t come out. What does come out makes her want to throw herself out the window because she’s a sucker for making Eunha happy. The pout Eunha’s sending her way is killing Yizhuo inside too.

“I think I’m free on the weekends to help you guys out.”

Eunha starts going off about where they’re all going to meet up, what food they should get before studying, after studying. Yizhuo’s stomach is doing backflips at how adorable she is.

And Aeri? She’s smiling, joking, playing along, all while looking at her with this dangerous glint in her eyes. Yizhuo’s stomach wants to throw up at the idea of what Aeri actually wants to do during that day.

Yizhuo feels like she just got locked into a route inside of a dating sim. And she did not like where it was going.

Yizhuo also needs a shower. A long, cold, soapy shower.

And a very lengthy, in-depth discussion with Gaeul about fire.

[uncooked-glass]

@nox-ut-lux sacrificing your exam results for me was worth it

I made @nox-ut-lux stay up for 30 minutes helping me with the background for this

Mumbo hates when his collar is uneven so B is fixing it for him :))

[icarusflewfirst]

Back at it after the Christmas hols

Inferential statistics is gonna be the death of me 🤯

they’re definitely not easing us back in at all. Have an assignment due tomorrow but it’s completed and submitted so fingers crossed it’s good because my brain is fried

Hope everyone has had a great day! x

[that-angry-noldo]

this noldo has passed inferential statistics with a 4 <3333

[thesargasmicgoddess]

Epiphanies on a Sunday:

Apparently, I can do inferential statistics at 43 years old but have seemingly forgotten how to manually divide decimals 🤣🤣

In my defense...it's only been 30 years or so since the last time I divided manually. ➗️➗️🤦🏻‍♀️

[werewolfetone]

I wish they would let you write "the table and the results of the inferential statistics tests which were run show that this is the dumbest experiment ever conceived of by man and everyone who works here is bad at collecting data" as your conclusion in a lab report

[anishmary]

In the subject of data analytics, this is the most important concept that everyone needs to understand. The capacity to draw insightful conclusions from data is a highly sought-after talent in today's data-driven environment. In this process, data analytics is essential because it gives businesses the competitive edge by enabling them to find hidden patterns, make informed decisions, and acquire insight. This thorough guide will take you step-by-step through the fundamentals of data analytics, whether you're a business professional trying to improve your decision-making or a data enthusiast eager to explore the world of analytics.

Step 1: Data Collection - Building the Foundation

Identify Data Sources: Begin by pinpointing the relevant sources of data, which could include databases, surveys, web scraping, or IoT devices, aligning them with your analysis objectives. Define Clear Objectives: Clearly articulate the goals and objectives of your analysis to ensure that the collected data serves a specific purpose. Include Structured and Unstructured Data: Collect both structured data, such as databases and spreadsheets, and unstructured data like text documents or images to gain a comprehensive view. Establish Data Collection Protocols: Develop protocols and procedures for data collection to maintain consistency and reliability. Ensure Data Quality and Integrity: Implement measures to ensure the quality and integrity of your data throughout the collection process.

Step 2: Data Cleaning and Preprocessing - Purifying the Raw Material

Handle Missing Values: Address missing data through techniques like imputation to ensure your dataset is complete. Remove Duplicates: Identify and eliminate duplicate entries to maintain data accuracy. Address Outliers: Detect and manage outliers using statistical methods to prevent them from skewing your analysis. Standardize and Normalize Data: Bring data to a common scale, making it easier to compare and analyze. Ensure Data Integrity: Ensure that data remains accurate and consistent during the cleaning and preprocessing phase.

Step 3: Exploratory Data Analysis (EDA) - Understanding the Data

Visualize Data with Histograms, Scatter Plots, etc.: Use visualization tools like histograms, scatter plots, and box plots to gain insights into data distributions and patterns. Calculate Summary Statistics: Compute summary statistics such as means, medians, and standard deviations to understand central tendencies. Identify Patterns and Trends: Uncover underlying patterns, trends, or anomalies that can inform subsequent analysis. Explore Relationships Between Variables: Investigate correlations and dependencies between variables to inform hypothesis testing. Guide Subsequent Analysis Steps: The insights gained from EDA serve as a foundation for guiding the remainder of your analytical journey.

Step 4: Data Transformation - Shaping the Data for Analysis

Aggregate Data (e.g., Averages, Sums): Aggregate data points to create higher-level summaries, such as calculating averages or sums. Create New Features: Generate new features or variables that provide additional context or insights. Encode Categorical Variables: Convert categorical variables into numerical representations to make them compatible with analytical techniques. Maintain Data Relevance: Ensure that data transformations align with your analysis objectives and domain knowledge.

Step 5: Statistical Analysis - Quantifying Relationships

Hypothesis Testing: Conduct hypothesis tests to determine the significance of relationships or differences within the data. Correlation Analysis: Measure correlations between variables to identify how they are related. Regression Analysis: Apply regression techniques to model and predict relationships between variables. Descriptive Statistics: Employ descriptive statistics to summarize data and provide context for your analysis. Inferential Statistics: Make inferences about populations based on sample data to draw meaningful conclusions.

Step 6: Machine Learning - Predictive Analytics

Algorithm Selection: Choose suitable machine learning algorithms based on your analysis goals and data characteristics. Model Training: Train machine learning models using historical data to learn patterns. Validation and Testing: Evaluate model performance using validation and testing datasets to ensure reliability. Prediction and Classification: Apply trained models to make predictions or classify new data. Model Interpretation: Understand and interpret machine learning model outputs to extract insights.

Step 7: Data Visualization - Communicating Insights

Chart and Graph Creation: Create various types of charts, graphs, and visualizations to represent data effectively. Dashboard Development: Build interactive dashboards to provide stakeholders with dynamic views of insights. Visual Storytelling: Use data visualization to tell a compelling and coherent story that communicates findings clearly. Audience Consideration: Tailor visualizations to suit the needs of both technical and non-technical stakeholders. Enhance Decision-Making: Visualization aids decision-makers in understanding complex data and making informed choices.

Step 8: Data Interpretation - Drawing Conclusions and Recommendations

Recommendations: Provide actionable recommendations based on your conclusions and their implications. Stakeholder Communication: Communicate analysis results effectively to decision-makers and stakeholders. Domain Expertise: Apply domain knowledge to ensure that conclusions align with the context of the problem.

Step 9: Continuous Improvement - The Iterative Process

Monitoring Outcomes: Continuously monitor the real-world outcomes of your decisions and predictions. Model Refinement: Adapt and refine models based on new data and changing circumstances. Iterative Analysis: Embrace an iterative approach to data analysis to maintain relevance and effectiveness. Feedback Loop: Incorporate feedback from stakeholders and users to improve analytical processes and models.

Step 10: Ethical Considerations - Data Integrity and Responsibility

Data Privacy: Ensure that data handling respects individuals' privacy rights and complies with data protection regulations. Bias Detection and Mitigation: Identify and mitigate bias in data and algorithms to ensure fairness. Fairness: Strive for fairness and equitable outcomes in decision-making processes influenced by data. Ethical Guidelines: Adhere to ethical and legal guidelines in all aspects of data analytics to maintain trust and credibility.

Data analytics is an exciting and profitable field that enables people and companies to use data to make wise decisions. You'll be prepared to start your data analytics journey by understanding the fundamentals described in this guide. To become a skilled data analyst, keep in mind that practice and ongoing learning are essential. If you need help implementing data analytics in your organization or if you want to learn more, you should consult professionals or sign up for specialized courses. The ACTE Institute offers comprehensive data analytics training courses that can provide you the knowledge and skills necessary to excel in this field, along with job placement and certification. So put on your work boots, investigate the resources, and begin transforming.

[stagnate-03]

Your Guide to Success in Quantitative Research: 8 Practical Tips

Quantitative research plays a crucial role in fields like social sciences, business, healthcare, and education. It provides numerical data that can be analyzed statistically to identify patterns, relationships, and trends. However, excelling in quantitative research requires more than just crunching numbers.

1. Start with a Clear Research Question

The foundation of any successful research is a well-defined research question. This question guides the entire study, determining your methodology, data collection, and analysis. Ensure that your research question is specific, measurable, and aligned with the purpose of your study.

For example, instead of asking, "How do students perform in school?" a clearer question might be, "What is the relationship between study hours and academic performance in high school students?"

Tip: Before starting, spend time refining your question. This will save you time and effort during the research process.

2. Choose the Right Research Design

Quantitative research can take many forms, including experiments, surveys, and observational studies. Choosing the right design depends on your research objectives and the type of data you need. Are you testing a hypothesis?

Tip: Match your research design with your objectives to ensure you’re collecting the right kind of data.

3. Use Valid and Reliable Instruments

The tools you use to gather data—whether they’re questionnaires, tests, or measuring devices—must be both valid (measuring what you intend to measure) and reliable (producing consistent results over time).

Tip: If you’re developing your own instrument, pilot it first with a small group to check its validity and reliability. If using an existing tool, review past studies to confirm it works well for your research population.

4. Select an Appropriate Sample Size

A common mistake in quantitative research is working with a sample size that’s too small, which can lead to unreliable or inconclusive results. On the other hand, excessively large samples can waste resources. To avoid these pitfalls, conduct a power analysis to determine the optimal sample size for your study.

Tip: Use tools like G*Power to calculate the right sample size based on your research goals and the expected effect size. This ensures your findings are statistically significant and applicable to a larger population.

5. Ensure Random Sampling for Representativeness

Your findings will only be meaningful if your sample represents the broader population you’re studying. Random sampling ensures that every individual in the population has an equal chance of being selected, reducing bias and increasing the generalizability of your results.

Tip: Use random sampling methods (e.g., simple random sampling, stratified random sampling) to ensure your data is as representative as possible.

6. Minimize Bias in Data Collection

Bias can creep into any research process, affecting the accuracy and fairness of your results. To reduce bias, carefully design your data collection process. For example, avoid leading questions in surveys and standardize how data is collected across all participants to prevent interviewer or observer bias.

Tip: Blind or double-blind studies can help minimize bias, especially in experiments where participants or researchers might be influenced by expectations.

7. Analyze Data Properly with the Right Statistical Tools

Once you’ve collected your data, the next step is analysis. Choosing the right statistical tests is essential to interpret your findings correctly. Descriptive statistics (like means and frequencies) give a broad overview, while inferential statistics (like t-tests, chi-squares, or regression analyses) help determine whether your findings are statistically significant.

Tip: If you’re unsure which test to use, consult a statistician or use resources like statistical decision trees to guide your choice based on your data type and research questions.

8. Interpret Results with Context and Caution

After analyzing your data, it’s tempting to jump to conclusions. However, quantitative research is not just about the numbers; it’s about what those numbers mean in context. Always interpret your results in relation to your research question and the existing body of knowledge.

Be cautious when generalizing your findings, especially if your sample size is small or non-representative. Additionally, consider the limitations of your study—were there any confounding variables, measurement errors, or external factors that might have influenced your results?

Tip: Be transparent about the limitations of your study. Acknowledging them strengthens the credibility of your research.

Conclusion

Mastering quantitative research requires attention to detail, a solid understanding of statistical methods, and a commitment to rigor throughout the process. By following these 8 practical tips—starting with a clear question, choosing the right design, using valid instruments, selecting the appropriate sample, minimizing bias, analyzing correctly, and interpreting results carefully—you’ll be well on your way to conducting successful and impactful quantitative research.

Read more: https://stagnateresearch.com/blog/how-to-excel-in-quantitative-research-8-essential-tips-for-success/

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