Extreme Value Theory: Understanding and Predicting Rare Events
Let's have a closer look at EVT, its applications, and its challenges.
prob-stats
How to Use the Cauchy Distribution in Python
In this tutorial, we’ll learn more about the Cauchy distribution, visualize its probability density function, and learn how to use it in Python.
Welch’s t-Test: The Reliable Way to Compare 2 Population Means with Unequal
Discover why Welch’s t-Test is the go-to method for accurate statistical comparison, even when variances differ.
Computing Minimum Sample Size for A/B Tests in Statsmodels: How and Why
A deep-dive into how and why Statsmodels uses numerical optimization instead of closed-form formulas
How the Guinness Brewery Invented the Most Important Statistical Method in
The most common test of statistical significance originated from the Guinness brewery. Here’s how it works
Mastering Statistical Tests
Your Guide to Choosing the Right Test for Your Data
9 key probability distributions in data science: Easy explanation
Understanding probability distributions in data science is crucial. They provide a mathematical framework for modeling and analyzing data.
Beyond the Bell Curve: An Introduction to the t-distribution
Discover the origins, theory and uses behind the famous t-distribution
Reliability Analysis with Python
Total Productive Maintenance
Mastering Monte Carlo: How To Simulate Your Way to Better Machine Learning
How a Scientist Playing Solitaire Forever Changed the Game of Statistics
SynthDiD 101: A Beginner’s Guide to Synthetic Difference-in-Differences
On the method’s advantages and disadvantages, demonstrated with the synthdid package in R
How to Understand and Use the Jensen-Shannon Divergence
A primer on the math, logic, and pragmatic application of JS Divergence — including how it is best used in drift monitoring
The Unreasonable Effectiveness of Conditional Probabilities
There are more ways to arrange a deck of cards than there are atoms
Think of your last card game – euchre, poker, Go Fish, whatever it was. Would you believe every time you gave the whole deck a proper shuffle, you were holding a sequence of cards which had never
How to Select the Right Statistical Tests for Different A/B Metrics
A Discussion of the go-to methods for 5 Types of A/B Metrics
Chi-Square Test to Compare Categorical Variables
Complete Guideline to Find Dependencies among Categorical Variables with Chi-Square Test
The statistical magic behind the bootstrap
How to use the bootstrap for tests or confidence intervals and why it works
Fully Mastering Fisher’s Exact Test for A/B Testing
While Fisher’s exact test is a convenient tool for A/B testing, the idea and results of the test are often hard to grasp and difficult to…
Heuristics That Almost Always Work
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Understanding CUPED
An in-depth guide to the state-of-the-art variance reduction technique for A/B tests
Home Page of Evan Miller
Articles, software, calculators, and opinions.
Implications of use of multiple controls in an A/B test
DAGs and Control Variables
How to select control variables for causal inference using Directed Acyclic Graphs
Sobol Indices to Measure Feature Importance
Understanding the model’s output plays a major role in business-driven projects, and Sobol can help
Confidence Intervals Simply Explained
A concise explanation of confidence intervals.
Statistical T-Test Simply Explained
An introduction to the Student’s t-distribution and the Student’s t-test
Log-normal Distribution — A simple explanation
How to calculate μ & σ, the mode, mean, median & variance
30 Probability and Statistics Interview Questions for Data Scientists
Top 30 Probability and Statistics Interview Questions that can help you sharpen your skills to ace your data science interview
A Simple Interpretation of p-values
P-values & ice cream consumption simply explained.
Probability Distributions with Python’s SciPy
How to Model random Processes with Distributions and Fit them to Observational Data
The curious case of Simpson’s Paradox
In 1996, Appleton, French, and Vanderpump conducted an experiment to study the effect of smoking on a sample of people. The study was conducted over twenty years and included 1314 English women…
Resource Round-Up: Causal Inference | Emily Riederer
Free books, lectures, blogs, papers, and more for a causal inference crash course
Box-Cox transformation is the magic we need
The smart trick to choose the right model
8 Common Pitfalls of Running A/B Tests
How not to fail your online controlled experimentation
Using strip charts to visualize dozens of time series at once
Strip charts are extremely useful to make heads or tails from dozens (and up to several hundred) of time series over very long periods of…
Project Lighthouse — Part 1: P-sensitive k-anonymity
Part one of a series on how we will measure discrepancies in Airbnb guest acceptance rates using anonymized perceived demographic data.
Log-Normal Distribution
A Log-Normal Distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed.
Understanding Parametric Tests, Skewness, and Kurtosis
This article introduces important subcategories of inferential statistical tests and discusses descriptive statistical measures related to the normal distribution.
7 Statistical Distributions that every Data Scientist should know— with intuitive explanations
Intuitive explanations for the Normal, Bernoulli, Binomial, Poisson, Exponential, Gamma and Weibull distribution — with Python example code
10 Normality Tests-Python (Step-By-Step Guide 2020)
Normality tests to check if a variable or sample has a normal distribution.
How to Calculate the KL Divergence for Machine Learning - MachineLearningMastery.com
It is often desirable to quantify the difference between probability distributions for a given random variable. This occurs frequently in machine learning, when we may be interested in calculating the difference between an actual and observed probability distribution. This can…
Seven Must-Know Statistical Distributions and Their Simulations for Data Sc
Assumptions, relationships, simulations, and so on
Overview of data distributions
With so many types of data distributions to consider in data science, how do you choose the right one to model your data? This guide will overview the most important distributions you should be familiar with in your work.
A quick refresher of statistical power
An easy-to-follow guide next time you forget how to do power calculations
NIST Handbook of Engineering Statistics
Lognormal, Weibull, and Gamma distribution in One Picture - DataScienceCentral.com
At first glance, the Lognormal, Weibull, and Gamma distributions distributions look quite similar to each other. Selecting between the three models is “quite difficult” (Siswadi & Quesenberry) and the problem of testing which distribution is the best fit for data has been…
The Student t-Distribution
The Power-Law Distribution
Classic Probability Problem #2: The Coupon Problem
Data Science & Machine Learning Interviews
A Complete Guide to Hypothesis Testing
From Controlling for Testing Errors to Selecting the Right Test