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Showing 21 - 40 of 44
Crash Course: Statistics #21 - P-Values, Part 1
Crash Course

In this episode we’ll talk about Null Hypothesis Significance Testing (or NHST) which is a framework for comparing two sets of information.

Crash Course: Statistics #24 - Bayes
Crash Course

Today we’re going to talk about Bayes Theorem and Bayesian hypothesis testing.

Crash Course: Statistics #22 - P-Values, Part 2
Crash Course

Today we’re going to discuss some problems with the logic of p-values, how they are commonly misinterpreted, how p-values don’t give us exactly what we want to know, and how that cutoff is arbitrary – and arguably not stringent enough in some scenarios.

Crash Course: Statistics #23 - P-Values, Part 3
Crash Course

We’re going to finish up our discussion of p-values by taking a closer look at how they can get it wrong, and what we can do to minimize those errors.

Crash Course: Statistics #27 - Matched Pair T-Tests
Crash Course

Today we’re going to walk through a couple of statistical approaches to answer the question: “is coffee from the local cafe, Caf-fiend, better than that other cafe, The Blend Den?”

Crash Course: Statistics #26 - Test Statistics
Crash Course

Today, we’ll introduce some examples using both t-tests and z-tests and explain how critical values and p-values are different ways of telling us the same information.

Crash Course: Statistics #25 - Bayes, Part 2
Crash Course

Today we’re going to finish up our discussion of Bayesian inference by showing you how we can it be used for continuous data sets and be applied both in science and everyday life.

Crash Course: Statistics #30 - P-Hacking
Crash Course

Today we’re going to talk about p-hacking (also called data dredging or data fishing).

Crash Course: Statistics #29 - Chi-Square Tests
Crash Course

Today we’re going to talk about Chi-Square Tests – which allow us to measure differences in strictly categorical data like hair color, dog breed, or academic degree.

Crash Course: Statistics #28 - Degrees of Freedom
Crash Course

Today we’re going to talk about degrees of freedom – which are the number of independent pieces of information that make up our models.

Crash Course: Statistics #32 - Regression
Crash Course

Today we’re going to introduce one of the most flexible statistical tools – the General Linear Model (or GLM). GLMs allow us to create many different models to help describe the world – you see them a lot in science, economics, and politics.

Crash Course: Statistics #33 - ANOVA
Crash Course

Today we’re going to continue our discussion of statistical models by showing how we can find if there are differences between multiple groups using a collection of models called ANOVA.

Crash Course: Statistics #31 - Replicability
Crash Course

Replication (re-running studies to confirm results) and reproducibility (the ability to repeat an analyses on data) have come under fire over the past few years.

Crash Course: Statistics #34 - Intersectional Groups
Crash Course

Last week we introduced the ANOVA model which allows us to compare measurements of more than two groups, and today we’re going to show you how it can be applied to look at data that belong to multiple groups that overlap and interact.

Crash Course: Statistics #35 - RMA and ANCOVA
Crash Course

Today we’re going to wrap up our discussion of General Linear Models (or GLMs) by taking a closer looking at two final common models: ANCOVA (Analysis of Covariance) and RMA (Repeated Measures ANOVA).

Crash Course: Statistics #36 - Machine Learing
Crash Course

We’ve talked a lot about modeling data and making inferences about it, but today we’re going to look towards the future at how machine learning is being used to build models to predict future outcomes.

Crash Course: Statistics #39 - Big Data Problems and Solutions
Crash Course

There is a lot of excitement around the field of Big Data, but today we want to take a moment to look at some of the problems it creates.

Crash Course: Statistics #38 - Big Data
Crash Course

Today, we’re going to begin our discussion of Big Data.

Crash Course: Statistics #37 - Unsupervised Learning
Crash Course

Today we’re going to discuss how machine learning can be used to group and label information even if those labels don’t exist.

Crash Course: Statistics #44 - Failed Predictions
Crash Course

Today we’re going to talk about why many predictions fail – specifically we’ll take a look at the 2008 financial crisis, the 2016 U.S. presidential election, and earthquake prediction in general.

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