shira mitchell

blog post:

probability sample = known nonzero probability

epsem = equal individual probabilities

SRS = equal entire-sample probabilities

Cover of book, titled „The Politics of Human Rights“ by Sabine Carey, Mark Gibney, and Anita Gohdes. Picture shows a woman kneeling in front riot police during a BLM protest in the US.

Coming soon: our introduction to the politics of human rights 🥳📚

Preorder available here: www.cambridge.org/highereducat...

@sabinecarey.bsky.social

blog post: quantity vs quality

compare 2 surveys:

1. 100% coverage, but response probability P[R = 1 | Y] differs a lot by Y

2. Only 5% coverage, but P[R = 1 | Y] is roughly constant across Y

which would you use ? both ?

new blog post: sampling the sample

we’ve focused on estimating means E[Y].

but say Y are openends ("describe how you feel about the candidate") and you want to read thru a few draws from the population, not only survey responders.

what should you do ?

blog post: weights and MRP for voters

so far we've talked about weights and MRP for E[Y], vote choice in the population overall.

but what if you want E[Y | V = 1], vote choice in the population of voters.

what are the weights and how do you modify MRP ?

Survey Statistics: continued struggles with equivalent weights
statmodeling.stat.columbia.edu/2025/11/04/s...

blog post: continued struggles with equivalent weights

blog post: Blue Rose Research is hiring !

We are looking for a teammate with expertise in both LLM tools and statistical modeling.

Someone who clearly communicates assumptions, results, and uncertainty. With care and kindness.

CC @swenkuh.bsky.social @avehtari.bsky.social

blog post: individualism doesn't work

typical machine learning loss looks at one individual at a time

but for MRP, we care about aggregates

kinda the flipside of last week's post: bsky.app/profile/shir...

CC @yajuansi.bsky.social @bradytwest.bsky.social

blog post: MRPW

you've got a survey collected by someone else, and they gave you weights.

how can you use those weights in the MRP (Multilevel Regression and Poststratification) ?

CC: @tslumley.bsky.social

blog post: struggles with equivalent weights

you've done MRP.

someone asks you for survey weights.

how to get them ?

CC @fontikar.bsky.social @njtierney.bsky.social 🥾

blog post: beyond balancing

in midterms, voters tend to support the out party for balance

do polls still help predict midterms ? yes

The ultimate New York City lover’s treasure hunt is back Friday, Oct. 17 through Sunday, Oct. 19: bit.ly/3IHnHwn

The Elephant in the Sampling Frame: Lessons from Basu's Parable In Basu's circus story (PDF), the owner wants to estimate the weight of fifty elephants and suggests weighing Sambo, who looked “average” three years ago, and reporting $50y$. A statistician counters ...

highly recommend reading @soodoku.bsky.social's post (even if it is about elephants and not bears)

www.gojiberries.io/lessons-from...

it was for me too !

blog post: Fat Bear Week

Basu's Bears is a lesson in:

1) using auxiliary information (pre-salmon-feasting weights)

2) how bad an unbiased estimator can be

statmodeling.stat.columbia.edu/2025/09/23/s...

CC: @shirokuriwaki.bsky.social @rnishimura.bsky.social

blog post: random sampling is not leaving

we turned to response instrument Z because random sampling is "dead"

but does this method still rely on starting with random sampling ?

blog post: random sampling is not leaving

we turned to response instrument Z because random sampling is "dead"

but does this method still rely on starting with random sampling ?

that makes sense !

Z0 = treatment at time 0
Z1 = treatment at time 1
Y1 = outcome at time 1
Y2 = outcome at time 2

teammate 1 is regressing Y1 on Z0
teammate 2 is regressing Y2 on Z0, Z1, Y1

teammate 2 wants Y1 not to be imputed with Y2, right ?
teammate 1 wants Y1 to be imputed with Y2, right ?

thanks, Lucy ! What if each teammate is running a different analysis with a different "Y" ? my "Y" might be your "X" ? would we have a different imputation procedure for each of us so that each person's "Y" is no used in imputing their "X" ?

blog post on imputation (again):

we want E[Y|X] but X can be missing

@lucystats.bsky.social @sarahlotspeich.bsky.social @glenmartin.bsky.social @maartenvsmeden.bsky.social et al. say:

random imputation should use Y
deterministic imputation shouldn't

statmodeling.stat.columbia.edu/2025/09/09/s...

I learn from every interaction with Maria. I attended a talk of hers yesterday. I highly recommend reading her work.

blog post: connections between survey statistics and experimental design.

split-plot designs are analogous to cluster sampling.

blocking is analogous to stratification.

featuring an experiment by Arjun Potter and colleagues at NM-AIST !

CC @davidbroska.bsky.social @austin-van-loon.bsky.social