Null Hypothesis Significance Testing
The third lecture in the series on the SPINE of statistics. We look at how model parameters can be used to test hypotheses by using the standard error to compute a test statistic and p-value. We look at what the p-value represents and some of its limitations. We disucss effect sizes as a useful context for p-values. Learn R alongside these lectures with the discovr package (https://www.discovr.rocks/discovr/) Suggested soundtrack: Black Crown Initiate: A Great Mistake ( • A Great Mistake )

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Effect sizes and Bayes factors

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Lecture 01: The General Linear Model

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Bayesian Inference: Overview

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Null Hypothesis, p-Value, Statistical Significance, Type 1 Error and Type 2 Error

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Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!

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Trump Preps for 80th Birthday, Threatens to Hit Iran, Knicks Historic Win & Elon Musk Trillionaire!?

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The medical test paradox, and redesigning Bayes' rule

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The problem with pretending quantum mechanics makes sense | Sean Carroll

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The Central Limit Theorem

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Statistical Significance and p-Values Explained Intuitively

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Power Analysis, Clearly Explained!!!

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Richard Feynman: Quantum Mechanical View of Reality 1

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The better way to do statistics | Bayesian #1

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Bayes theorem, the geometry of changing beliefs

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P-values and significance tests | AP Statistics | Khan Academy

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t-Tests Using SPSS

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How To Know Which Statistical Test To Use For Hypothesis Testing

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158,962,555,217,826,360,000 (Enigma Machine) - Numberphile

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