Bayesian thinking is about answering the right question – the probability of a hypothesis being true of false.
Alzheimer's Disease has had many failures, and various companies have had mixed results. Bayesian approaches can bring clarity to the inference and primary question: "Does this treatment work?"
Implicit models in the back of our minds can creep into explicit models creating biased predictions that have societal implications.
If we fail to acknowledge that we have biases and assumptions that influence our assessment of 'objective facts,' then we delude ourselves. Our perception of reality and how we judge evidence is colored by our beliefs which arise from our specific experiences.
The probability that the null hypothesis is true is 0.50. How should we interpret that and then write it down mathematically?
You may have heard, “Always do subgroup analysis, but never believe them.” Don't believe this.
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?
