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
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."
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.
Published research from respectable journals and reported by renowned press outlets can be very misleading and of questionable importance. But it helps keep funding for the researchers and readership for the news media.
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?"
Estimands is the simplest concept with the most difficult implementation. With the release of the final ICH Guideline in Estimands today, this first of several blogs to follow delves into the topic.
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.