📚 The concept of statistical power and effect size are important in hypothesis testing.
⚖️ Statistical power refers to the probability of correctly rejecting the null hypothesis when it is false.
📏 Effect size measures the magnitude of the difference between groups in a study.
📊 Differences between groups may be due to sampling error rather than population differences.
❌ Type 1 error occurs when the null hypothesis is rejected incorrectly.
✅ Accepting the null hypothesis may lead to type 2 error.
📊 Statistical power refers to the probability of rejecting the null hypothesis when it is false.
🔍 The statistical power is influenced by the size of the sample and the effect size.
📈 Increasing the sample size enhances the statistical power and the ability to detect significant differences.
📝 The difference between statistical power and effect size.
🔬 The importance of effect size in hypothesis testing.
📏 How effect size is measured using a formula.
📊 The video explains the concept of statistical power and effect size.
🔍 Effect size measures the magnitude of the difference between two groups.
📈 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.
📏 The concept of effect size and its importance in statistical analysis.
⚖️ The use of power analysis to determine the sample size needed for detecting an effect.
🔍 The significance of interpreting the magnitude of an effect in addition to hypothesis testing.
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