Power and Error Calculations in Hypothesis Testing and Statistics with Examples: What is Power(sensitivity) in Statistics and How to Calculate it? What factors influence errors in hypothesis testing and power of the test? How can we increase power of a test in research and statistics?
👉🏼 Errors and Power in Statistics Video: ( https://youtu.be/OYbc3uKpGmg ); Sensitivity, Specificity, Positive and Negative Predictive Values Video (https://youtu.be/eeM7KPRNlSs)
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In this statistics video lecture we learn about statistical power of a hypothesis test and type II error in statistics and in research.This tutorial covers the concept of power in statistics, how statistical power can be calculated, and the factors that affect power.
Here, we explore more in detail how the Power is related to alpha, the sample size (n), and the difference we wish to detect. The goal is to use this as a foundation for understanding the concept of power, and the factors that affect it. While power calculations can become quite complicated very quickly, the underlying concept is always the same. This video should lay a foundation for understanding power as a concept.
Some of these terminology can be confusing at first. when "rejecting a null hypothesis", this is referred to as a "positive test result". "failing to reject the null" is a "negative test result" (much like disease testing, null is that you don’t have disease, alternative is that you do have the disease, and testing positive means we reject the null and conclude that you have the disease, and vice versa). A Type I error is when we reject the null when in reality it is true. when we reject the null this is a "positive test result" and if in reality this is incorrect, it is a "false positive".
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