I came across this post explaining what a 70% chance of rain means. I understand the concept, but it got me wondering: what other statistical concepts sound simple but are commonly misunderstood or misinterpreted by the general public?

If you plan to use any US-govt-produced health-related datasets, download them ASAP. The social vulnerability index (SVI) dataset on the ATSDR web page is already gone; and it is rumored that this is part of a much more general takedown.

Wasn't sure where to post this - apologies if it is a violation of the rules.

I am a senior psychology student and taking Statistics II course this semester. And I am really having difficulties. I am really awful at maths and I cannot figure out the logic behind all these, especially ANOVA. In case I fail the course, this might delay my graduation and it stresses me out terribly. What should I do? I am really beginning to think that I have an intellectual disability. I need urgent help.

I would love your feedback so that I can make this as useful as possible.

Here's the link to the channel: https://www.youtube.com/channel/UCzEmcsawWvM1GGesDCeX_lQ
Here are the first three main videos:

• Covid-19 Exponential Growth and Compound Interest: Covid-19 Exponential Growth and Compound Interest: https://youtu.be/OKWKd09bk30

• Stop Focusing on Covid-19 Case Counts | Focus on Percent Positive Instead.: https://youtu.be/IqeGJjKjXT4

• Covid-19 Testing Accuracy: Worse Than You Think: https://youtu.be/9mOq9d-eXxI

I also made a short video explaining who I am and why I'm doing this: https://youtu.be/sLIquDwwTpw

Regardless of whether you like the content or not, I'd love your feedback. I can only improve if I know what works and what doesn't. Thanks!

I often see students being very confused about this topic. Why do you think this happens? For what it’s worth, here’s how I usually try to explain it:

The p-value doesn't directly tell us whether H₀ is true or not. The p-value is the probability of getting the results we did, or even more extreme ones, if H₀ was true.
(More details on the “even more extreme ones” part are coming up in the example below.)

So, to calculate our p-value, we "pretend" that H₀ is true, and then compute the probability of seeing our result or even more extreme ones under that assumption (i.e., that H₀ is true).

Now, it follows that yes, the smaller the p-value we get, the more doubts we should have about our H₀ being true. But, as mentioned above, the p-value is NOT the probability that H₀ is true.

Let's look at a specific example:
Say we flip a coin 10 times and get 9 heads.

If we are testing whether the coin is fair (i.e., the chance of heads/tails is 50/50 on each flip) vs. “the coin comes up heads more often than tails,” then we have:

H₀: coin is fair
Hₐ: coin comes up heads more often than tails

Here, "pretending that Ho is true" means "pretending the coin is fair." So our p-value would be the probability of getting 9 heads (our actual result) or 10 heads (an even more extreme result) if the coin was fair,

It turns out that:

Probability of 9 heads out of 10 flips (for a fair coin) = 0.0098

Probability of 10 heads out of 10 flips (for a fair coin) = 0.0010

So, our p-value = 0.0098 + 0.0010 = 0.0108 (about 1%)

In other words, the p-value of 0.0108 tells us that if the coin was fair (if H₀ was true), there’s only about a 1% chance that we would see 9 heads (as we did) or something even more extreme, like 10 heads.

(If there’s interest, I can share more examples and explanations right here in the comments or elsewhere.)

Also, if you have suggestions about how to make this explanation even clearer, I’d love to hear them. Thank you!

I’m curious how many people had a moment where a concept they once memorized mechanically suddenly made intuitive sense much later — and it changed how they saw the rest of statistics.

For me it was realizing that a confidence interval isn’t “the probability the parameter is in the interval,” but a statement about the procedure. Once that clicked, hypothesis testing, p-values, and even power started feeling less mysterious and more like different views of the same machinery.

I’ve also heard people say things like:

• Understanding variance as “average squared distance” instead of a formula.

• Seeing regression as “conditional averages” rather than line-fitting.

• Interpreting probability as long-run frequency vs. degree of belief.

What was yours? A concept that went from “I can compute this” to “oh… I actually get it now,” and made other topics fall into place. I feel like those delayed “aha” moments are where most real learning in stats actually happens.

I see a lot of posts on here about R vs Python and it seems like the consensus is "both are good - if you want a job in academia, learn R, and if you want a job elsewhere, learn Python." I'm wondering, though, if there's any reason to learn R at all if I already have some experience in Python. Is there anything that I can do in R that I can't do (or can't do easily) in Python?

For context (why I'm asking), I'm a developer outside of the statistics space. I thought it'd be cool to create some statistical analysis tools for the team. I did my undergrad in statistics years ago and we did a lot of cool stuff in R. I'm keen on finding an excuse to use it again, but looking online it's hard for me to see any really clear advantages to the language.

I haven't really been able to find a good and recent answer (without the context of which to pick for a potential career) about this so I made an account here just to ask.

I've calculated and recalculated multiple times, multiple ways and I just don't understand how I keep getting a p value of 6.5 in excel. Sample size 500, mean is 1685.209, hypothesized mean is 1944, std error is 15.73. I'm using the =t.dist.2t(test statistic, degrees of freedom) with the t statistic -16.45, sample size is 500 so df is 499... and I keep getting 6.5 and don't understand what I'm doing wrong. Watching a step by step video on how to calculate and following it word for word and nothing changes. Any ideas how I am messing up? I know 6.5 is not a possible p value but I don't know where I'm going wrong. TIA

So understand what the text is saying here but when I try to find other examples to practice online of standard deviation almost every source uses the notation for standard error, sigma.

Is this book just using its own notation or is there a widespread agreement of the difference of standard error and standard deviation and their notation?

i found this data set at https://www.kaggle.com/datasets/valakhorasani/mobile-device-usage-and-user-behavior-dataset and I dont think the scatter plot is supposed to look like this

I understand it's not an average. I'm not going to live another 20 years. It's either a shorter or a longer life. But does it mean I have a 50/50 shot of making it to my retirement age (67 in my country), for instance? Is there a bell curve? I was never good at statistics and would like to understand it better.

EDIT: My sincere apologies for my late reply. January was a horrendous month for me and I was emotionally fragile (winter depression hit me harder than ever this year) when I wrote this post. I'm doing a lot better now.

Thank you for the wonderful replies and well wishes. I will read through the posts, the articles mentioned and whatever the Monty Hall problem is.

I have been informed by experts in the field that, yes, I should keep new developments in oncology, radiology and immunotherapy in mind. Let's hope for the best!

I review a fair amount of applied quantitative work and I keep running into the same pattern: tables full of p-values and significance stars, but no standardized effect sizes, no confidence intervals around the estimates, nothing that tells you whether the effect actually matters in practice. A regression coefficient of 0.002 with p < 0.001 tells me the sample is large, not that the effect is interesting.

I know the ASA put out a statement on this years ago, and I've seen plenty of arguments for reporting effect sizes. But the practice hasn't really changed in a lot of fields. Is there a reason people still find p-values alone informative? Or is it just institutional inertia at this point - reviewers expect stars, so authors provide stars?

Is there any coefficient that tells if there is a non-linear relationship between two variables the same way that Pearson's correlation coefficient summarizes the linear relationship between two variables? If not, what would be the most effective way to detect/ summarize a non linear relationship between two variables?

I was watching this video explaining the respawn date of the demons in DOOM. In short, there’s a 2.6% chance of an enemy respawning every second. This YouTuber used this formula to calculate the time it would take for there to be a 50% cumulative chance of an enemy respawning: ~26 seconds.

I played around with geometric and exponential distributions but they’re not giving me what I’m looking for. For example: using the above formula, changing 0.5 to x, would give the time with cumulative x probability of an enemy respawning.

What type of distribution can be used like that?

I'm sure many of you have firsthand experience with this scenario: you spend a lot of time investigating a dataset and find some interesting correlations, maybe even run some regressions or other models that output useful results, and want to present your findings at your place of work.

After you have presented what you have found, Karen from marketing remarks that she doubts the results because "correlation isn't causation". John from HR remembers this phrase from his high school days and agrees. You try to explain to Karen and John why causation is not relevant here, and correlation alone is enough to fit models and make accurate predictions. Sadly, neither Karen nor John have the statistical knowledge necessary to correctly interpret what you just said and stick with their own caricaturistic understanding of what the phrase "correlation isn't causation" means. Sally, the meeting secretary, remarks that maybe these issues would be resolved if the sample size was larger.

Often the only things that people remember decades after taking a statistics 101 class is that 1. correlation isn't causation, and 2. the sample is never large enough, often while genuinely being convinced that these two critiques undermine advanced statistical methods.
At this point I don't think they serve as just useful caveats anymore, they weaken the general public's understanding of what statistics and its possibilities and limitations are. Perhaps it should be left to the statistician to judge whether the sample size is adequate or if correlation suffices.

So, should the emphasis be shifted towards the amazing power of statistical methods and away from over-generalized caveats regarding their use? (while still teaching the caveats, of course).

Saw a board that was used together with a darts target, probably over several years. I would expect the missed shots are uniform around the circumference, but on image they are not - maybe players target some high value sectors, and the missed shots are normally distributed around these targeted areas. Maybe there are some other biases.

Two questions:

1. what is a good distribution to fit this kind if data to (imagine I had the coordinates of each missed shot)

2. if I wanted to use this example for central limit theorem, how would I go about the random misses should converge to a normal distribution. can these missed shots be normal in any sense (eg distance from center)?

many thanks in advance

I'm currently working on my masters thesis and i'm a bit baffled by this plot. My dependent variable is on a 5 step discrete scale due to it being survey data hence the 5 lines i gather but im having a hard time figuring out the conclusion i make from the plot in general.

You've got standard deviation which instead of being the mean of the absolute values of the deviations from the mean, it's the mean of their squares which then gets rooted. Then you have the coefficient of determination which is the square of correlation, which I assume has something to do with how we defined the standard deviation stuff. What's going on with all this? Was there a conscious choice to do things this way or is this just the only way?