Understanding Statistical Power and Effect Size in Hypothesis Testing and Clinical Studies

📚 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.