Type I and Type II Errors in Statistical Decision Making
In this video, we'll talk about the four possible outcomes of making your decision after a hypothesis test. Specifically, we'll focus on the two errors you can make: the Type I Error (aka "False Alarm" or "Alpha Error") and the Type II Error (aka "Miss" or "Beta Error"). We'll also take a moment to mention the two correct decisions you can make, known as "Statistical Power" and "Specificity." Finally, we'll discuss what influences each of these four possible outcomes and why errors are possible in the first place.

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What is the One-Sample T-Test?

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Establishing a Standard of Evidence (Alpha Levels)

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Forming Statistical Hypotheses (Null and Alternative)

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

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Strengths and Weaknesses of Data Visualizations (Graphs)

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Evaluating Hypotheses in Light of Evidence (P-Values)

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What are Linear Regressions?

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Forms of Reliability in Research and Statistics

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How to Calculate a One-Way ANOVA by Hand

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Hypothesis Testing EXPLAINED

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How To Identify Type I and Type II Errors In Statistics

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What Is a P-Value? A Simple Explanation!

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Difference Between Hypothesis Tests and Effect Sizes

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How To Think SO CLEARLY People Assume You're A Genius

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Measures of Variability (Range, Standard Deviation, Variance)

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Intro to Hypothesis Testing in Statistics - Hypothesis Testing Statistics Problems & Examples

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How To Know Which Statistical Test To Use For Hypothesis Testing

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AlphaFold - The Most Useful Thing AI Has Ever Done

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