๐ The concept of statistical power and effect size are important in hypothesis testing.

โ๏ธ Statistical power refers to the probability of correctly rejecting the null hypothesis when it is false.

๐ Effect size measures the magnitude of the difference between groups in a study.

๐ Differences between groups may be due to sampling error rather than population differences.

โ Type 1 error occurs when the null hypothesis is rejected incorrectly.

โ Accepting the null hypothesis may lead to type 2 error.

๐ Statistical power refers to the probability of rejecting the null hypothesis when it is false.

๐ The statistical power is influenced by the size of the sample and the effect size.

๐ Increasing the sample size enhances the statistical power and the ability to detect significant differences.

๐ The difference between statistical power and effect size.

๐ฌ The importance of effect size in hypothesis testing.

๐ How effect size is measured using a formula.

๐ The video explains the concept of statistical power and effect size.

๐ Effect size measures the magnitude of the difference between two groups.

๐ A high effect size indicates a strong difference between the groups.

Meta-analyses are valuable resources in psychology to determine the effectiveness of a certain therapy.

Meta-analyses analyze multiple research studies to calculate the effect size of a therapy.

Statistical power is used to determine the sample size needed to minimize the probability of committing a type 2 error.

๐ The concept of effect size and its importance in statistical analysis.

โ๏ธ The use of power analysis to determine the sample size needed for detecting an effect.

๐ The significance of interpreting the magnitude of an effect in addition to hypothesis testing.

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