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