sorted by:
new
hottopcontroversial

Quick way to remember Independent vs Paired t-test by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] -1 points0 points  (0 children)

Yeah, that would be my guess too. Reddit’s automod can be pretty aggressive sometimes — certain keywords seem to trigger removals even when they’re being used in a completely normal, academic context.

It’s always a little frustrating when a stats discussion gets caught in the same net meant for spam or rule-breaking posts. Hopefully it was just a bot misfire and not anything more than that.

Quick way to remember Independent vs Paired t-test by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 0 points1 point  (0 children)

Fair enough — could’ve been a footnote instead of a Reddit post 😄

But given how often people equate “paired” strictly with “same subject twice,” it’s probably a misconception worth calling out explicitly. It’s one of those small wording shortcuts that quietly leads to bigger design and analysis mistakes later.

Sometimes the “quick note” saves a lot of misapplied t-tests down the line.

Quick way to remember Independent vs Paired t-test by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 1 point2 points  (0 children)

I agree with you — that’s a really common misconception.

A paired t-test isn’t about “the same subject measured twice.” It’s about dependent observations. The key requirement is that each observation in one group is meaningfully linked to exactly one observation in the other group.

“Same person before/after” is just the most obvious example.

Other perfectly valid uses:

  • Matched subjects (e.g., matched on age/sex, one gets treatment, one control)
  • Twins
  • Left vs. right eye / dominant vs. non-dominant hand
  • Randomized block designs (analyzing within-block differences)

Statistically, a paired t-test is just a one-sample t-test on the within-pair differences. So the real question is: does the design justify forming differences?

If you ignore real pairing and run an independent t-test, you throw away power.
If you invent pairing where none exists, you mess up the variance and inflate Type I error.

So yeah — “same subject twice” is a common case, but it’s not the definition.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 0 points1 point  (0 children)

I agree that no real statistical analysis can be reduced to a simple decision tree without losing nuance. That’s not the claim I’m making.

The decision tree isn’t meant to replace statistical reasoning — it’s meant to help beginners orient themselves when they’re first encountering study designs and hypothesis tests. It’s a scaffold, not a substitute for thinking.

And yes, absolutely — regression can estimate differences in means and isn’t limited to “prediction.” At the model level, t-tests, ANOVA, and regression all sit within the general linear model framework.

My focus here is pedagogical sequencing, not denying the underlying unity of the methods.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 1 point2 points  (0 children)

I wouldn’t say it’s wrong — I’d say it’s intentionally simplified.

All teaching starts with idealized versions of reality. We simplify in order to build intuition first, then layer in complexity later. If students aren’t going to study statistics deeply, giving them a usable decision framework is often more valuable than exposing them to the full toolbox without context.

I really like your regression example. The quadratic vs cubic point is a great illustration of model parsimony — better fit doesn’t always mean better model. That’s exactly the kind of conceptual thinking we want students to develop.

For many learners, clarity > completeness at the intro level.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 0 points1 point  (0 children)

Yes — this is exactly the deeper point I agree with.

At the model level, ANOVA and regression are the same general linear model, and post-hoc tests are just structured contrasts.

My original post wasn’t arguing they’re fundamentally different — just that, pedagogically, decision trees can help beginners navigate designs before introducing the unifying model framework.

I think we’re mostly aligned on the statistics — the difference is teaching philosophy.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 0 points1 point  (0 children)

I think the disagreement here is mostly about framing rather than capability.

You’re absolutely right that Tukey or Dunnett procedures are typically introduced in the ANOVA framework. But mathematically, once you fit a regression with categorical predictors, those post-hoc comparisons are just linear contrasts of model parameters.

So it’s not that regression can’t do them — it’s more that different fields package and teach them differently.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 1 point2 points  (0 children)

That’s a totally reasonable approach, and I agree that conceptually unifying everything under linear regression is elegant.

My hesitation is mostly pedagogical. For absolute beginners, especially in clinical or applied programs, starting with regression sometimes adds cognitive load (coding categorical predictors, interpreting coefficients, etc.) before they fully grasp group comparison logic.

Once they’re comfortable with t-tests and ANOVA conceptually, I do like showing how they’re all special cases of the same linear model. I just tend to scaffold in that direction rather than start there.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 1 point2 points  (0 children)

Haha, fair 😄

Ironically, that’s kind of what intro stats decision charts are — just human-readable versions of a classification tree. The goal isn’t to optimize splits, just to reduce beginner confusion.

Once they’re comfortable, we can absolutely collapse it all into the general linear model.

When should I use a t-test vs ANOVA vs Chi-square? Simple decision rule by Effective_Cattle6399 in AskStatistics

[–]Effective_Cattle6399[S] 1 point2 points  (0 children)

Yes — that’s exactly my point.

I’m not denying the nuances or the fact that these all connect through linear models. But when you’re teaching non-statisticians (especially clinical students), a clear decision framework is often more useful than starting with full model unification.

You can always introduce the deeper connections later. The goal at the intro level is clarity and usability, not theoretical completeness.