#statstab #486 Testing Bayesian Informative Hypotheses in Five Steps With JASP and R {bain}
Thoughts: The BAIN module let's you go beyond "effect vs no effect" by specifying contrasts (hyp) & obtaining fractional BFs.
#bain #jasp #bayesfactor #bayesian #rstats #r #hypothesis #nhbt #BF #methods #tutorial #guide
https://share.google/cTDvBO7SQM9CpNqlU
#statstab #467 Hypothesis testing, model selection, model comparison some thoughts
Thoughts: An excellent (but too short) discussion on bayesian inference.
#bayesian #bayesfactor #modelselection #inference #NBHT #BF #ROPE #primer
#statstab #453 {Bayes Power}
A General Application of Power and Sample Size Calculation for the Bayes Factors
Thoughts: Blending frequentist notions of power with bayes hypothesis testing.
#poweranalysis #bayesian #bayesfactor #errorrate #rstats #nhbt
#statstab #443 Dienes Bayes factor calculator
Thoughts: Dienes presents a different way to compute BFs using the sample data. But, this can be seen as an acceptable double-dipping.
#statstab #402 On Bayes factors for hypothesis tests {emBayes Factor}
Thoughts: On bsky there were renewed debates about BFs. This paper provides "better" priors (mixture t centred on the ES). Also some p-value BFs
#bayesian #bayesfactor #priors #cohend
https://link.springer.com/article/10.3758/s13423-024-02612-2
#statstab #382 The JASP Guidelines for Conducting and Reporting a Bayesian Analysis
Thoughts: @JASPStats is often people's first attempt at Bayesian statistics. But proper inference and reporting is crucial.
#JASP #Bayesian #BayesFactor #guide #tutorial
https://link.springer.com/article/10.3758/s13423-020-01798-5
#statstab #360 Bayes Factor Design Analysis {bfda}
Thoughts: Sample size planning is confusing at first with Bayesian. But BFDA is the quick answer.
#statstab #359 A Pragmatic Approach to Statistical Testing and Estimation (PASTE)
Thought: A (basic) guide to some alternatives to p-values: bayesian posterior intervals, Bayes Factors, and AIC.
#statstab #346 Jeffreys-Lindley paradox
Thoughts: I like this short explanation of the "paradox" of why frequentist and bayesian inference can differ.
#paradox #frequentist #bayesian #inference #bayesfactor #pvalue #explanation
https://michael-franke.github.io/intro-data-analysis/jeffreys-lindley-paradox.html
Biosignature Hype
I was thinking just the other day that I haven’t posted much in either the Astrohype or the Bad Statistics folders on this blog. Well today I found an item that belongs in both categories. Many people will have seen the widespread press coverage of a misleading claim of the discovery of alien life; see, e.g., here. This misleading press coverage is based on a misleading press release from the University of Cambridge which you can find here.
The story is based on a paper in the pay-to-publish Astrophysical Journal Letters with the title “New Constraints on DMS and DMDS in the Atmosphere of K2-18 b from JWST MIRI“. The DMS and DMDS in the title refer to Dimethyl Sulphide and Dimethyl Disulphide respectively. These are interpreted by the authors as biosignatures.
There are two main problems with this claim. One is that DMS and DMDS are not necessarily biosignatures in the first place; see here for the reasons. The other is that there isn’t even any evidence for the detection of DMS or DMDS anyway. Here is the spectrum of which the lead author of the paper, Prof. Nikku Madhusudhan, has claimed “the signal came through loud and clear”.
Yeah, right. In statistical terms this is a non-detection. The Bayes Factor used in the paper to quantify the evidence for a model with DMS and/or DMDS over one without is just 2.62 in the logarithm. That’s not a detection by any stretch of the imagination; to be anywhere near convincing a Bayes Factor has to be at least 100. The subsequent cherry-picking of the data to improve the apparent probability of a detection is just statistical flummery.
Notice that the use of the phrase “Constraints on” in the title of the paper does not indicate that the article presents evidence that a detection has been made. That the claim has somehow morphed into the “the strongest evidence for life beyond our solar system” is absurd. The most charitable thing I can say is that Prof. Madhusudhan must have been carried away by enthusiasm. This doesn’t reflect very well on Cambridge University either.
This episode worries me greatly. This is a time of increasing hostility towards science and this sort of thing can only make matters worse. Scientists need to be much more careful in communicating the uncertainties in their results.
UPDATE: There’s a now paper on arXiv here that argues that a straight line is a better fit to the data, in other words that there is no strong statistical evidence for spectral features at all.
#BayesFactor #Biosignatures #DimethylDisulphide #DimethylSulphide #ProfNikkuMadhusudhan
#statstab #9 Francis (2017) Equivalent statistics and data interpretation
Thoughts: I've a love-hate relationship w the paper & app. It's *technically* true but promotes a bad stats argument. Still useful.
#stats #pvalues #ttest #effectsize #bayesfactor
https://www1.psych.purdue.edu/~gfrancis/EquivalentStatistics/
