Inferential Statistics Articles

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Niklas Donges Niklas Donges
Updated on December 18, 2024

Intro to Descriptive Statistics for Machine Learning

What you need to know to get started with descriptive statistical analysis.

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Anmolika Singh Anmolika Singh
Updated on August 08, 2024

Guide to Power Analysis and Statistical Power

Power analysis is a process that involves evaluating a test’s statistical power to determine the necessary sample size for a hypothesis test. Learn more.

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Richard Farnworth Richard Farnworth
Updated on August 01, 2024

The Birthday Paradox Explained

The birthday paradox is the probability theory that the probability of two people sharing the same birthday grows with the number of possible pairings. 

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Adam Thomas Adam Thomas
Updated on June 26, 2024

The Kruskal Wallis Test: A Guide

The Kruskal-Wallis test is an important statistical analysis for data that doesn’t follow normal distributions. Our expert shows you how it works here.

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Jeremy Zhang Jeremy Zhang
Updated on January 17, 2024

Importance Sampling Explained

Importance sampling is an approximation method that uses a mathematical transformation to take the average of all samples to estimate an expectation. Here’s how to do it.   

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Terence Shin Terence Shin
Updated on December 21, 2023

Statistical Bias: 6 Types of Bias in Statistics

Statistical bias is when a model or statistic is unrepresentative of the population. There are six main types of bias in statistics.

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Anthony Corbo Anthony Corbo
Updated on November 07, 2023

What Is Inferential Statistics?

Inferential statistics is the practice of using sampled data to draw conclusions or make predictions about a larger sample data sample or population.

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Jørgen Veisdal Jørgen Veisdal
Updated on October 09, 2023

The Pigeonhole Principle Explained

The pigeonhole principle states that if n items are put into m containers, with n > m, then at least one container must contain more than one item.

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Sara A. Metwalli Sara A. Metwalli
Updated on March 15, 2023

4 Probability Distributions Every Data Scientist Needs to Know

If you’re just getting started on your journey toward becoming a data scientist, these are the 4 most common distributions you’ll encounter.