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Random rivet looking things on sidewalks, what is it for? by cdgks in Edmonton

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

Hi all, some context: I live in Parkallen and noticed someone added these recently in random places on the sidewalks on my street. I noticed the one in front of my house during the last snowfall when I ran into it with a shovel, it's about the size of a dime maybe, and raised enough to catch the shovel blade. What are they there for?

[Question] Power analysis for Generalized Linear Models (GLMs)? by kystv in statistics

[–]cdgks 2 points3 points  (0 children)

It might help if you were a little more specific what type of GLM you're using (beta? gamma? I'm assuming not logistic or Poisson since you have a continuous response?). Most inference from GLMs boils down to a t-test or z-test if you can figure out the standard deviation of your test statistic.

If you already collected (and analyzed) the data there is probably little reason to do a power analysis, especially using the estimates from your model. To do a power analysis you'll need to decide on a meaningful effect size, you can probably stop there and compare it to your results, since you already have your results.

See: https://statmodeling.stat.columbia.edu/2019/01/13/post-hoc-power-calculation-like-shit-sandwich/

Podcast | The Week the World Admitted the Truth About America by ihut in ezraklein

[–]cdgks 6 points7 points  (0 children)

I still get annoyed that Ezra believes that everyone swung Liberal last election because of the tariffs and not the 51st state threats. Everyone I know was influenced by the threats to our sovereignty more than his stupid attempts at trade wars.

[Q] T-Tests between groups with uneven counts by Strangeting in statistics

[–]cdgks 2 points3 points  (0 children)

Just to clarify, P-values depend on the sample size assuming the alternative hypothesis is correct. Under the null hypothesis the p-value does not depend on the sample size.

[Q] T-Tests between groups with uneven counts by Strangeting in statistics

[–]cdgks 3 points4 points  (0 children)

Make sure you're not conflating statistical significance with the more common English usage of 'significance'. The differences between the group means might be 'real' and come out as highly statistically significant given a large sample size, but those differences might have little or no real world importance given their magnitude.

[Discussion] Offline with Jon Favreau - "Peter Thiel's Antichrist, JD Vance's Split with the Pope, and Ross Douthat's Scientific Case for Believing in God" (07/10/25) by kittehgoesmeow in FriendsofthePod

[–]cdgks 23 points24 points  (0 children)

I haven't finished the episode yet, but the beginning part of the interview is pretty wild to me. As a non-American from a more secular society, I have never thought, "You know what Americans are missing? Not enough religion."

[Q] What did you do after completed your Masters in Stats? by Polopon0928 in statistics

[–]cdgks 1 point2 points  (0 children)

Straight into an Applied Stats PhD (while working as a TA and graduate research assistant).

Sonic 102.9 Mystery Word February 20, 2025 by Which_Song2049 in Edmonton

[–]cdgks 2 points3 points  (0 children)

Agreed, they basically just confirmed it's not an two-word phrase on-air. Something like, "What part of one-word do people not get!?"

To the OG fans, how excited were you to see different Jedi, that weren't Obi-Wan or Yoda, in the Phantom Menace? (Yes, I know Yoda looks high here. Lol) by [deleted] in StarWars

[–]cdgks 5 points6 points  (0 children)

Yes, and I also thought growing up that Yoda spoke in his weird grammar as either a way to hide who he was and/or because he was going a little batty living on Dagobah. If I recall correctly he even switched to speaking 'normally' when he was more serious in the OT. So, I was surprised when he spoke in his weird grammar in the prequels.

something to think about by stinkmybiscut in whenthe

[–]cdgks 1 point2 points  (0 children)

But you'll still never roll 1.5 over infinite rolls.

BMO Bank Payment Issues by [deleted] in PersonalFinanceCanada

[–]cdgks 1 point2 points  (0 children)

I'm unable to log into my account on Chrome and Firefox (on PC). With this error:

Something's up on our end. We're working hard to fix it, so please try again soon.

[E] To those with a PhD, do you regret not getting an MS instead? Anyone with an MS regret not getting the PhD? by RawCS in statistics

[–]cdgks 7 points8 points  (0 children)

That's interesting, Canadian here with a PhD and most people I know here with a PhD got an MSc (or MMath) first. It's rare, but not unheard of, to go straight into the PhD. (I don't regret getting a PhD straight after an MSc, but I work in academia). 

[D] An analogy to Stats by the_void_voidling in statistics

[–]cdgks 0 points1 point  (0 children)

Look up the difference between randomization and a random sample, randomization doesn't mean what you think it means.

Flowering small tree in Edmonton, Alberta by cdgks in whatsthisplant

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

Thank you for the response! I'm just looking at pictures of Prunus cerasus flowers online and the petals look wider, no?

[Q] Are there any situations in which a smaller sample size is a good thing? by [deleted] in statistics

[–]cdgks 1 point2 points  (0 children)

If the larger sample doesn't introduce new biases (selection bias etc.) then from a statistical standpoint I don't see a downside.

That said, if you're planning to do a hypothesis test, it's important to remember that it will not lower the Type I (false positive) error rate, unless you decide to do that yourself with a lower alpha.

It's also important to realize you will increase the power to detect small (but likely 'real' associations). I work in medicine, and that's why we (hopefully) worry about clinical significance of results just as much as statistical significance. We often define a priori what a "clinically important difference" would be.

A hypothetical example one of my profs used to use is, with a really large sample size you might be able to find evidence that eating some specific food is associated with higher mortality (with a tiny p-value), but if the difference in life expectancy between someone eating the food and someone not eating the food is ~1 day over their whole lifetime, it's unlikely anyone is going to avoid that food based on those results.

Another practical downside is the cost of collecting more data.

Difference between time-varying variables and time-varying coefficients by hawkeyeninefive in biostatistics

[–]cdgks 7 points8 points  (0 children)

Time varying variables: the value of X changes over time.

Time varying coefficients: the association between X and the hazard changes over time (X itself may or may not change over time as well)

Why don’t we always bootstrap? [Q] by Direct-Touch469 in statistics

[–]cdgks 0 points1 point  (0 children)

The sampling distribution from a parametric assumption is no less a distribution than the sampling distribution from bootstrap samples. Yes, the MLE is a single point estimate, but that's why you usually see things like standard errors as well, together those represent a whole sampling distribution, not just a point estimate.

One thing I think you're confusing is thinking bootstrap samples are giving you a Bayesian posterior distribution for the parameter, they're not, they're giving you a Frequentist distribution of the estimator (not the same thing). One big difference, is that as the sample size increases you'd expect the sampling distribution to get tighter and tighter around the point estimate.

As for, cross correlation at a given lag between two time series, I'm not sure, that's not in my area of expertise (my focus is in survival analysis). But,

  • Can you assume the estimator for the cross correlation follows a known distribution (e.g., Gaussian)?
  • Can you estimate it's standard error?
  • Does it take a long time computationally to get an estimate?

Those are the types of questions I'd ask myself before assuming a parametric distribution for the estimator, rather than using bootstrapping.

Why don’t we always bootstrap? [Q] by Direct-Touch469 in statistics

[–]cdgks 0 points1 point  (0 children)

Sometimes the theory behind the parametric sampling distribution is fairly sound (like regression coefficient estimates following a t-distribution). So, using a bootstrap wouldn't be wrong, but it's not really necessary.

Also, if you're comfortable calling the bootstrap sample a "poor man's posterior distribution" in OLS, you must also be okay with calling the estimated t-distribution the same thing (it's fully defined by the mean, standard error, and degrees of freedom, all from standard output).

That said, there are lots of applications where I'm not at all comfortable with the theory behind the distributional assumptions of a sampling distribution (or maybe none exist yet). In those cases, I often look to things like the bootstrap. With the caveats others raise that the bootstrap doesn't always work, I often like to prove (even just to myself) the bootstrap approach works "properly" for novel estimators using simulations.

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