The Battle For The Soul Of Causal Inference
battle_for_the_soul_of_causal_inference_blog.knit The Battle for the Soul of Causal Inference: Pearl vs. Rubin In causal inference methodology, an intellectual battle of titans has been unfolding for decades. This conflict isn’t merely academic…
Association Does Not Imply Causation, Except When it Does – A Causal Inference Perspective
association_to_causation_blog.knit The Challenge of Causal Inference Ever wondered why researchers are so cautious when saying “X causes Y” instead of just “X is associated with Y”? The difference isn’t just…
Common DAG Structures–Confounding, Collider Bias, and Mediation
common_DAG_structures_blog.knit Introduction Directed Acyclic Graphs (DAGs) are powerful tools for visualizing and understanding causal relationships. In this blog post, we’ll explore common DAG structures that frequently appear in causal inference…
Selection Bias, A Causal Inference Perspective (With Downloadable Code Notebook)
collider_bias.knit Find the RMarkdown Notebook on Github and Run the Code Yourself! Introduction - What is Collider Bias? Collider bias occurs when we condition on (or select based on) a…
Simulating The Distribution of P-Values (With Downloadable R Code Notebook)
p.value_distribution_simulations.knit Introduction In this simulation, we will investigate the distribution of p-values : both when the null hypothesis is true. The idea is simply to simulate a sample size of…
Tell Me About Yourself
A Data Scientist answered an interview question… I had an hour to kill before my meeting, so I decided to stop by the university student center. That random visit turned…
What Are P-values And Why Are They So Problematic?
Introduction P-values are probably the most discussed statistical topic in history. They are often criticized, distrusted, misused, misinterpreted, and, on the flipside, used everyday in every single empirical study. So,…
Adding the “Real” to Real-World Data
The Irony of “Real-World Data” Have you noticed how many companies now include “AI” as a buzzword on their websites or in their product descriptions? How many do you think…
Power Calculation : 8 Reasons Why You Should Care About It
What is a power calculation good for? Power calculation helps a researcher determine the required sample size for a study. It is done at the planning stage of a trial.…
The Complete Biostatistics Roadmap, or How to Become a Great Biostatistician (with Books and Resources)
I’m often asked by budding biostatisticians curious and eager to learn more to advise them on a developmental roadmap. Here is what I think you need to become a great…
The Influence of Confounding Variables in Observational Studies
Observational studies play an important role in understanding associations between exposures and outcomes, particularly in fields where randomized controlled trials (RCTs) may not be feasible due to ethical, practical, or…
Do Power Calculation Before Data Collection, Not After – With Downloadable R Code Notebook
Don’t Compute the Statistical Power of Your Experiment…Even if SPSS Allows It And Your Editor Requires It! Introduction Download the R Markdown notebook here used to generate this blog post…
