Statistical inference and sampling assumptions Real probability samples have two great benefits: (i) they allow unbiased extrapolation from the sample; (ii) with data internal to the sample, it is possible to estimate how much results are likely to change if another sample is taken. These benefits, of course, have a price: drawing probability samples is hard work. An investigator who assumes that a convenience sample is like a random sample seeks to obtain...
Read More »Fisher’s exact test (student stuff)
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Read More »Significance testing and the real tasks of social science
Significance testing and the real tasks of social science After having mastered all the technicalities of regression analysis and econometrics, students often feel as though they are masters of the universe. I usually cool them down with the required reading of Christopher Achen’s modern classic Interpreting and Using Regression. It usually gets them back on track again, and they understand that no increase in methodological sophistication … alter the...
Read More »Death penalties and homicides — a D-I-D analysis
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Read More »John Snow and the birth of causal inference
John Snow and the birth of causal inference [embedded content] If anything, Snow’s path-breaking research underlines how important it is not to equate science with statistical calculation. All science entails human judgment, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of statistics is actually zero — even though you’re making valid statistical inferences! Statistical models are no...
Read More »Multilevel modeling (student stuff)
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Read More »Model validation and significance testing
Model validation and significance testing In its standard form, a significance test is not the kind of ‘severe test’ that we are looking for in our search for being able to confirm or disconfirm empirical scientific hypotheses. This is problematic for many reasons, one being that there is a strong tendency to accept the null hypothesis since they can’t be rejected at the standard 5% significance level. In their standard form, significance tests bias against...
Read More »Machine learning cross-validation (student stuff)
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Read More »Use of covariates in RCTs (wonkish)
Use of covariates in RCTs (wonkish) .[embedded content]
Read More »Bayes vs classical statistical p-testing
Bayes vs classical statistical p-testing .[embedded content] [For more on the RCT referred to in the video, take a look here. Mortality numbers are, of course, important, but so is the fact that among the 241 patients who received the drug, 52 developed severe illness, compared to 43 of 249 patients who did not take the drug … ]
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