Welcome to the Analytix Thinking blog! The blog that is intended to help people think rightly about data and deciding what is true. First, the intended audience is those who are analytically/quantitatively minded, but the exposition is also meant to be consumable by those who are curious about such matters, but without formal mathematical, statistical … Continue reading No. 1: Introduction – Welcome to Analytix Thinking
No. 25 – Estimands Part 2 – What Exactly Is an Estimand?
The word estimand has exploded into the vernacular of statistical thinking for clinical trials in the last decade or so. Where did it come from and why is it so important now?]
No. 24: Estimands Part 1 – Just Do ITT?
Randomization and complete data on the randomized experimental units are needed for cause-and-effect inference. What happens when that logic is broken?
No. 23: How Bayes Bets on Football
Bayesian and frequentist approaches to inference or prediction are very different. How different? This simple example highlights the difference and the argument in favor of using Bayesian posterior probabilities.
No. 22B: Whose Boat Is It Anyway?
Statistical Science and Data Science: Don't compete; CREATE!
No. 22A: Statistics and Data Science – The Two Cultures
Building a bridge between the Statistical and Data Science professions, or perhaps recognizing that the two cultures are manifestations of the same essence.
No. 21: Good News, Bad News, Worse News
Everyone likes to have their data analysis work result in some notable findings. Beware! "Torture the data long enough and they will confess to anything."
No. 20: I Am (Probably) Wrong, Maybe
A promising treatment for Covid-19 comes from a most unusual source - an anti-depressant treatment. Is the evidence compelling? What should we believe?
No. 19: We Won’t Get Fooled Again, Again
Many clinician researchers are attempting to "repurpose" old treatments for COVID-19. How shold we evaluate purported positive findings in a small, but rigorous, clinical trial?
No. 18: Analytics, Fast and Slow
Fast to the wrong answer is not a good business or scientific strategy. Slow, but rigorous, analysis does not meet business or scientific needs either. It has to be "and."
No. 17: Analytics, Data Science and Statistics – A Rose by Any Other Name …
There is a lot of confusion over what data science is and how it is the same or different from statistics or other data analytic fields such as epidemiology or econometrics. This is my attempt to describe the "big tent" of Analytics.
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