#p-value

[economicshomework]

How Can Understanding Effect Sizes Improve Your Statistics Homework

When working on your stats assignment, you’ve probably encountered situations where you’re asked to decide whether a result is significant. Maybe you ran a t-test and got a p-value of 0.03. That’s significant at the 0.05 level—but does that really tell you how big the result is?

This is where effect sizes come in. They go beyond a simple “yes or no” answer from statistical significance and give you a way to interpret results. Understanding effect sizes can not only improve your stats thinking but also make your stats homework more informative and precise.

In this article, we’ll break down effect sizes in a way that makes sense, using real-life examples. So, next time you think, “Can someone help me for statistics homework on effect sizes?”—you’ll already have the answers!

Why Effect Size Matters More Than Just Statistical Significance in Your Stats Homework

A common misconception among students is that a statistically significant result always means a big or important result. That’s just not true.

Here’s an example:

Suppose you compare the test scores of two groups of students and find that Group A scores higher than Group B, with a p-value of 0.049. Significant? Yes.

But what if the actual difference between the means of the two groups is just 0.5 points on a 100-point test? Meaningful? Not really.

Effect size tells us how big or important this difference is, not just whether it exists. This makes your stats assignments more nuanced and complete.

Types of Effect Sizes and When to Use Them in Your Stats Assignments

Depending on the type of analysis you’re doing, there are different measures of effect size you can use. Let’s go through the most common ones you’ll encounter.

1. Cohen’s d: How Much of a Difference Between Two Groups?

If you’re working with t-tests (comparing two groups), Cohen’s d is your go-to effect size measure. It tells you how far apart the two group means are in terms of standard deviations.

Formula for Cohen’s d:

d = (Mean of Group 1−Mean of Group 2)/Pooled Standard Deviation

Example:

You’re comparing the test scores of two different teaching methods:

Old Method: Mean = 75, SD = 10

New Method: Mean = 80, SD = 10

d=80−75/10=0.5

According to rules of thumb:

0.2 = Small

0.5 = Medium

0.8+ = Large

So, here we have a medium effect size, so the new method has some impact.

2. Pearson’s r: How Strong is the Relationship Between Variables

When you’re doing correlation analysis, Pearson’s r measures the strength and direction of the relationship between two variables.

Example:

If you analyze the relationship between study hours and exam scores and find r = 0.6, that means there’s a moderate to strong positive correlation—more study hours means higher scores.

But if r = 0.1, even though it’s statistically significant, the effect size is so small that studying more won’t make much of a difference.

3. R² (Coefficient of Determination): How Well Does Your Model Explain Variance

In regression analysis, R² tells you how much of the variance in the dependent variable is explained by the independent variable(s).

Example:

If you build a model predicting final exam scores based on attendance rate, and R² = 0.85, that means 85% of the variance in exam scores is explained by attendance—very strong!

If R² = 0.20, only 20% of the variance is explained, so there’s other factors to consider.

How Knowing Effect Size Helps You Score Higher on Statistics Assignments

You might be wondering—how does knowing effect sizes actually help me on my statistics assignments?

1. Helps You Interpret Results Better

Just reporting a p-value without an effect size is incomplete. Professors love when you go the extra mile to explain how big a result is, not just whether it’s statistically significant.

2. Avoids Misleading Conclusions

If you only focus on statistical significance, you might misconstrue a result. A tiny but statistically significant effect doesn’t mean it’s important in practice.

3. Strengthens Your Research and Data Analysis Skills

Effect sizes are used in real research, psychology, medicine, economics. Master them now and you’ll be ahead when dealing with real data in your future career.

Hands-On Example: Let’s Apply Effect Size to a Simple Statistics Homework

Problem: Comparing Two Study Methods

Suppose you do a study comparing two study techniques:

Your t-test gives a p-value = 0.04, so the difference is statistically significant. But let’s calculate the effect size (Cohen’s d):

d = (82−78)/{(12+10)/2}=411≈0.36

A d of 0.36 means small to moderate effect size. While the result is statistically significant, the actual effect of study methods isn’t big.

This extra layer of explanation will impress your professor and help you shine in your statistics homework!

Conclusion: Next Time You Think “Help Me for Statistics Homework,” Remember Effect Sizes

Effect sizes add depth to your statistical analysis. They go beyond “significant vs. not significant” and help you understand the practical impact of your results. So next time you work on a statistical problem or look for someone who can help you with statistics assignment, don’t just stop at the p-value—calculate the effect size and make your analysis more meaningful!

[eyes-of-nine]

my one seasonal illustration for this year feat. the boy ever

short hair version under the cut

[violetsoup]

minecraft testificates loosely inspired by 天狗　(tengu, creature in japanese mythology)

please reblog if you can! it helps me reach people outside :-P

[the-real-list-of-ominous-threats]

I’m going to make your p-value 40

[xagave]

Can I interest you in some Troll Currency

[locusfandomtime]

Doing the maths: Grian's failure at getting a mending book

lots of talk about maths and probabilities below the cut! but there's a graph and simple explanation at the end if you want to get the gist of it and are bad at maths.

(I am still young and learning maths, critique/advice always welcomed)

What are the odds of getting a mending book in Minecraft?

(I am assuming Grian has been doing all his fishing with Luck of the Sea 3)

The probability of a mending book is actually a bit annoying to estimate. The Minecraft Wiki lists fishing up an enchanted book as 1.9% chance. This is for ANY enchanted book. The Minecraft wiki talks about how the chance of an enchantment being selected is calculated. Mending has a weight of 2. Using the table, mending has a probability of 2/135.

However, Grian is looking for any book with mending, not just a pure mending book. Additional enchantments are calculated in a different way, involving RNG, which means it won't be as easy to model. Due to this reason, I'll just be using the odds for a pure mending book throughout.

TLDR: a mending book has a 0.028..% chance (2/135*0.019*100)

Grian's Data

According to this screenshot, Grian has used a fishing rod 5679 times. This number may not be fully accurate, as it includes the times he's fished other players, rather than just fished for items, but it is a good estimate.

To help visualise this data, with a median waiting time between catches of 17.5 seconds, Grian has spent over 20 hours fishing so far! He may have a problem.

Is this statistically significant?

Hypothesis testing (p-value approach):

H0: p = 19/67500 (the null hypothesis - he has no mending books because of chance)

H1: p < 19/67500 (the alternate hypothesis - he has no mending books due to different odds)

5679 trials, 0 mending books

X ~ B(5679, 19/67500) (binomial distribution, 5679 tries with a probability of a mending book being 19/67500, where X is the number of mending books)

p(X=0) (what is the probability the number of mending books being 0)

p = 0.2021473392

Now, the point at which data becomes significant is subjective. For instance, you *could* get a million heads in a row flipping a coin, it's not impossible, but at a certain point, you can begin to say "okay there's something not normal about this". For this approach, the closer the p-value is to 0, the more evidence there is against the null hypothesis . The p-value here is far above a significance level of 0.01, or 0.05, or 0.1. There isn't a clear line between significant/non-significant, but this is answer is quite a bit far from 0

With this, I cannot reject the null hypothesis.

Personal conclusion: this is not statistically significant, Grian is just unlucky.

Are other values statistically significant?

Gem's proposed 9000: results in a p-value of 0.079... more significant than Grian's number but I don't imagine Mojang would be too concerned. As said though, it's all subjective.

I am bad at maths, what does all this mean?

Here is a graph, showing what number of mending books you might have after 5679 tries. The height of the bar represents the probability of getting that amount. The numbers at the top are the (rounded) numbers I used in my calculation

The pink column is 0 mending books - like what Grian has! As you can see, it is less likely than getting 1 or 2 books, but not too uncommon to happen.

End conclusion: Grian has bad luck. Like, not as hilariously bad as he thinks, but still bad. If he keeps going, chances are he will get a mending book, but I think he should probably stop fishing because at this point he has a problem.

[emlan]

Romeo and the triplets are having yakiniku today 🥩 (the youngest sure is an annoying know-it-all… But he's only making sure his favorite brother has the best experience~)

I just assume everyone on earth has read Mob Psycho 100 but if not here's the original pages: [reddit link]

[turtleblogatlast]

Leo getting hit with a truth curse but instead of forcing him to admit to super sad or worrying things it’s things like “it was me who broke the remote” “I saw Mikey prank Donnie and helped hide it because it’s way funnier if he didn’t know who it was” “I rip my clothes to look more like Raph’s because he’s really cool” “my stripes aren’t even red they’re pink!”

[celestial-knight]

Lies of May: V. City

[13eyond13]

you ever think about how Misa's lack of intelligence and the way she lets her emotions impulsively dictate her actions was kinda constantly made the butt of the joke in DN, yet she genuinely outlived all the dudes and dudettes who were always scoffing at her stupidity. Somehow this is even better to me in that she wasn't even trying to outlive Light and didn't have any particularly high regard for her own life, so it's not due to her secretly being the best strategist of them all intentionally either... Regardless of the author's intent behind it I like that it pokes some holes in their constant smug assertions that you must have the very highest IQ to be the last guy standing in the murder notebook game

[howlsofbloodhounds]

Something about Colors integrity soul and justice soul being very similar but different in that integrity is internally focused and justice is externally focused.

Integrity holds color and the other souls and itself accountable and responsible by ensuring they adhere to the agreed upon moral values and act accordingly to the rules they set for themselves.

Meanwhile Justice is externally focused, holding others responsible and accountable for their actions and behaviors—especially towards color and the other souls and themself.

[thedeafprophet]

fanfiction is a fun and creative art format and i think writing and reading it has inherent value, regardless of whether it compares to 'real' writing or whatever nonsense. creation in and of itself has value

[keii4ii]

My Lies of P experience, part A

[thepersonalwords]

Your Core Values are the "glue" that holds your business together.

Doris P. Johnson

[venigni]

The fact that they only ever ask for one (1) gold coin fruit is so pathetic, petition to make it be at least 8 (a full cycle).

[creatingsweetthings]