# Archives

## HIRSCHAUER, GRÜNER, & MUßHOFF: Fundamentals of Statistical Inference: What is the Meaning of Random Error?

This blog is based on the book of the same name by Norbert Hirschauer, Sven Grüner, and Oliver Mußhoff that was published in SpringerBriefs in Applied Statistics and Econometrics in August 2022. Starting from the premise that a lacking understanding…

## GOODMAN: Ladies and Gentlemen, I Introduce to You, “Plausibility Limits”

Confidence intervals get top billing as the alternative to significance. But beware: confidence intervals rely on the same math as significance and share the same shortcominings. Confidence intervals don’t tell where the true effect lies even probabilistically. What they do…

## To Your List of Biases in Meta-Analyses, Add This One: Accumulation Bias

[From the preprint “Accumulation bias in meta-analysis: the need to consider time in error control” by Judith ter Schure and Peter Grünwald, posted at arXiv.org] “Studies accumulate over time and meta-analyses are mainly retrospective. These two characteristics introduce dependencies between…

## KIM & ROBINSON: The problem isn’t just the p-value, it’s also the point-null hypothesis!

In Frequentist statistical inference, the p-value is used as a measure of how incompatible the data are with the null hypothesis.  When the null hypothesis is fixed at a point, the test statistic reports a distance from the sample statistic…

## REED: P-Values: Come, Let Us Reason Together

Like many others, I was aware that there was controversy over null-hypothesis statistical testing. Nevertheless, I was shocked to learn that leading figures in the American Statistical Association (ASA) recently called for abolishing the term “statistical significance”. In an editorial…

## GOODMAN: Your p-Values Are Too Small! And So Are Your Confidence Intervals!

An oft-overlooked detail in the significance debate is the challenge of calculating correct p-values and confidence intervals, the favored statistics of the two sides. Standard methods rely on assumptions about how the data were generated and can be way off…

## Disagreeing With Disagreeing About Abandoning Statistical Significance

[From the preprint “Abandoning statistical significance is both sensible and practical” by Valentin Amrhein, Andrew Gelman, Sander Greenland, and Blakely McShane, available at PeerJ Preprints] “Dr Ioannidis writes against our proposals to abandon statistical significance…” “…we disagree that a statistical…

## Don’t Abandon It! Learn (and Teach) to Use It Correctly

[From the paper “The practical alternative to the p-value is the correctly used p-value” by Daniël Lakens, posted at PsyArXiv Preprints] “I do not think it is useful to tell researchers what they want to know. Instead, we should teach…

## Do Not Abandon Statistical Significance

[From the article “The Importance of Predefined Rules and Prespecified Statistical Analyses: Do Not Abandon Significance” by John Ioannidis, published in JAMA] “A recent proposal to ban statistical significance gained campaign-level momentum in a commentary with 854 recruited signatories. The…

## IN THE NEWS: Undark (March 21, 2019)

[From the article “Stats Experts Plead: Just Say No to P-Hacking” by Dalmeet Singh Chawla, published in Undark] “For decades, researchers have used a statistical measure called the p-value — a widely-debated statistic that even scientists find difficult to define — that is…