The over-reliance on p-values can lead to misinterpretation of data and a $150 million bet on a subgroup with scant evidence.
How do we know when an observed effect is real or spurious?
Some people say, "A p-value=0.05 is not very much evidence against the null hypothesis." Well then, how much evidence is it?
pr(B|A) ≠ 1 – pr(A|B). Why do we act like it ?!
For too long statisticians have been peddling pr(data|hypothesis) when scientists [indeed all of us] want pr(hypothesis|data).
What if AI in healthcare is the next asbestos?
It is easy to get an answer; it is very difficult to assess the quality of that answer.
Protect yourself from polio ... don't eat ice cream!
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