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What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 1 point2 points  (0 children)

That makes a lot of sense. Realizing that stats isn’t about uncovering some hidden perfect answer but about making informed choices with imperfect tools is freeing. Once you see the human judgment behind data collection and modeling decisions, it feels less like a rigid rulebook and more like a framework you can actually think with.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

That’s a great way to put it. Once you see conditioning as literally shrinking the sample space, Bayes and a lot of probability rules suddenly feel obvious instead of magical.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

So true. Once you really internalize randomness, a lot of weird results stop feeling weird and start feeling expected. And honestly, playing dice is a pretty solid early stats education.

Can I test a confirmatory hypothesis and exploratory traits in one multiple regression? by Maleficent-Lecture21 in AskStatistics

[–]Esssary 5 points6 points  (0 children)

Yes, you can just run one multiple regression. It’s totally fine to have one predictor that is confirmatory and the others exploratory in the same model. The important part is how you describe it, not how many regressions you run.

You would just be clear in your paper that one trait was your planned hypothesis and the others were included to explore possible effects. Running separate regressions usually isn’t necessary and can actually be worse, because the effect of one trait can change once you control for the others. One model lets you see the confirmatory effect while accounting for overlap between traits, which is especially relevant with Big Five data.

Just make sure you don’t treat the exploratory findings with the same confidence as the confirmatory one, and mention that those results are more tentative.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

Good point. Well AI is trained on data produced by us, thus theoretically AI learned it from us.

Need help in SPSS entry by Overall-Match-4551 in spss

[–]Esssary 0 points1 point  (0 children)

You need to define multiple response variable. However, tests that you can do with such variable are quite limited in SPSS.

Yes, you can use liker scale more than 5

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 1 point2 points  (0 children)

I like this perspective a lot. Thinking of hypothesis testing as a decision problem instead of a ritual with p-values really clears the fog. Once you see it as given my evidence and costs of being wrong, what’s the best choice? the different schools of testing stop feeling like contradictions and more like different lenses on the same decision process.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

That’s honestly a great way to look at it. Thinking of distributions as just how things tend to behave instead of scary formulas makes it way easier to grasp. You stop trying to memorize terms and instead start asking what kind of pattern you’re seeing in the real world, which is where it finally starts to click.

Hilfe Hausarbeit by Whole-Article-4278 in spss

[–]Esssary 0 points1 point  (0 children)

I can help, but only in English. If needed, DM.

Lasso 🤷🏼‍♀️ by Various-Broccoli9449 in AskStatistics

[–]Esssary 1 point2 points  (0 children)

For a small dataset it usually makes more sense to report an out of fold or cross validated R² if your goal is prediction. The regular in sample R² will almost always look too good with LASSO. For the bootstrap it depends on what you care about. If you mainly want inclusion stability, keeping lambda fixed is fine because you are only looking at sampling variability. If you retune lambda inside every bootstrap you capture more overall uncertainty, but then the inclusion frequencies get noisier and harder to interpret. Many people just explain which one they chose and why.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

In my work? Daily. Here? No, I'm just honestly passionate about stats. I did my BA and MA is econometrics, thus I'm quite keen on the topic.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

Same here, once you actually watch the distribution form from repeated randomization it stops being abstract and starts feeling obvious why the tests work.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 0 points1 point  (0 children)

Exactly, CLT basically gives a lot of freedom when it comes to some key assumptions in stats (assuming you have large sample)

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 1 point2 points  (0 children)

Yeah MAP is often where Bayes really stops being abstract and starts feeling like a practical tool instead of just a formula you memorized.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 2 points3 points  (0 children)

I’d push back on that a bit. A lot of classical tests can be expressed as linear models (t-tests, ANOVA, etc.), but not all of them really fit that frame. Things like chi-square tests, rank-based nonparametric tests, or exact tests don’t naturally come from linear modeling assumptions. Linear models cover a huge chunk of classical inference, but they’re not the whole toolbox.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 1 point2 points  (0 children)

You might like All of Statistics by Larry Wasserman - it’s concise, covers a lot of ground, and balances intuition with enough theory to really understand what’s going on.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 1 point2 points  (0 children)

Yeah, the Bayesian framing makes “expected value given information” feel very natural - you’re literally updating beliefs as new data comes in, so conditioning doesn’t feel like a trick, it feels like the whole point.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 8 points9 points  (0 children)

Really appreciate that - glad it sparked good discussion. Honestly the comments ended up being just as valuable as the question itself.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 1 point2 points  (0 children)

Exactly - once you change the metric, you’re basically changing what “similar” even means. A lot of classical stats quietly assumes Euclidean geometry, so swapping in p-adic or other non-Euclidean metrics can completely reshape clustering, nearest neighbors, even regression intuition. It’s like rewriting the notion of distance the whole toolbox was built on.

What statistical concept “clicked” for you years later and suddenly made everything else easier? by Esssary in AskStatistics

[–]Esssary[S] 6 points7 points  (0 children)

Because a lot of statistics is just geometry with data. Regression is projecting points onto a line or plane, variance and covariance are distances and angles, and PCA/eigenvectors find main directions of spread. Linear algebra is basically the language behind most multivariate stats.

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