A neophyte’s self-reflection — Every year, thousands of students around the globe — some willingly, some unwillingly — enroll in introductory statistics/data analytics/consumer analytics courses. And it’s not difficult to find students who would put these courses on top of their scariest academic experiences. In this article, I try to take the perspective of…

A neophyte’s self-reflection — This week, as I am wrapping up another semester in the applied social sciences, I have been revisiting some of the key challenges I faced over the last thirty months while transitioning from engineering to social science. In this article, I discuss some key differences between typical engineering data and…

A detailed explanation with toy examples — I. Correlation is “not sufficient” for causation (i.e., two “significantly” correlated variables may not have a causal relationship) In non-experimental data, despite the fact that — X and Y are strongly correlated/associated (coefficient close to/equal to 1) X and Y are (statistically) significantly associated (p-value of the coefficient less than 0.05)

An intuitive explanation — In my previous article, I discussed why you should prefer reporting 95% confidence interval over p-value, especially when you are explaining the findings of your study to non-statistician readers/audiences. In this article, I continue the discussion further and try to provide a bit more clarity. Why You Should Prefer Confidence Interval over p-value Communicating Results of Your Statistical Analysistowardsdatascience.com

Communicating Results of Your Statistical Analysis — The journey of an analyst in the world of data science, fundamentally, consists of three phases: learning, doing, and presenting. These phases are not necessarily linear; of course, most of us go back and forth. Which of these three phases do you perceive to be the most difficult one? Personally…

An intuitive explanation of the regression estimator — In case you have taken an introductory applied econometrics/statistics/data science course, you must be familiar with the interpretation of the coefficient of a predictor in a linear model estimated using regression. Today, I am going to share an intuitive example to highlight the fact that — just like this author…

An intuitive explanation — While interpreting the coefficient of one of the predictors (say a continuous variable X1) of an empirical linear model — with multiple explanatory variables (X1, X2, …..Xn) …

A tutorial to explore the question — Before starting to write this article, I googled “does money buy happiness?” Here is a snippet of what I found: It turns out money may buy some happiness (CNN) Here’s How Money Really Can Buy You Happiness (Time) Can Money Buy Happiness? Research Says: Yes, up to a Point (Insider) …

Applied Causal Inference 101: Non-Experimental Data — In the first part of this article, we discussed some of the core ideas of identifying the causal effect of doing something (such as receiving a treatment, implementing a policy, etc.). If you missed it, here is the link to the first part: How to Explore the Effect of Doing Something? (Part 1) Causal Inference 101: The Experimental Idealvivdas.medium.com