(2) hypothesis testing

• What is the aim of hypothesis testing?

  To review main concepts in hypothesis testing
• What are the steps in hypothesis testing?

  State hypotheses, set criteria, collect data, evaluate
• What are the two types of hypotheses in hypothesis testing?

  Null hypothesis and Alternative hypothesis
• What does the null hypothesis (H0) state?

  There is no true difference between samples
• What is the experimental hypothesis (H1)?

  The difference between means is real and significant
• What is a uni-directional hypothesis?

  Predicts a difference and states the direction
• What is a bi-directional hypothesis?

  Predicts a difference without stating direction
• When is a one-tailed test appropriate?

  If an effect in the untested direction is implausible
• What is the significance level (alpha, α) in hypothesis testing?

  Probability threshold for rejecting the null hypothesis
• What does a p-value less than 0.05 indicate?

  Unlikely to have occurred by chance
• What is a Type I error?

  Rejecting the null hypothesis when it is true
• What is a Type II error?

  Not rejecting the null hypothesis when it is false
• What factors affect statistical power?

  Significance level, sample size, effect size
• What does a small p-value indicate?

  Data unlikely under the null hypothesis
• What is the purpose of the standard error of the mean (SE)?

  To assess how well a sample represents the population
• What does a large standard error indicate?

  High variability between sample means
• What does a small standard error suggest?

  Sample means are similar to population mean
• What is the role of Z-scores in hypothesis testing?

  To compare scores across different samples
• What does a 95% confidence interval (95% CI) represent?

  Range where population mean falls in 95% of samples
• What are the steps in hypothesis testing?

  1. State the hypotheses
  2. Set the criterion for a decision
  3. Collect sample data and calculate statistics
  4. Evaluate the null hypothesis
• What are the differences between Type I and Type II errors?

  • Type I error: Rejecting H0 when it is true (false positive)
  • Type II error: Not rejecting H0 when it is false (false negative)
• What is the relationship between populations and samples?

  • Populations: Entire group of interest
  • Samples: Subset of the population
  • Parameters: Characteristics of populations
  • Statistics: Characteristics of samples
• What are the types of sampling methods?

  • Probability sampling: Random, Stratified, Systematic, Cluster
  • Non-probability sampling: Quota, Snowball, Convenience
• What is the significance of effect size in hypothesis testing?

  • Indicates the magnitude of the difference
  • Larger effect size increases statistical power
• What is the importance of sample size in hypothesis testing?

  • Larger samples reduce Type II errors
  • Smaller samples may lead to biased results
• What is the purpose of reporting confidence intervals?

  • To provide a range for the population mean
  • To assess the precision of the sample estimate
• What is the relationship between sample statistics and population parameters?

  • Sample statistics estimate population parameters
  • Parameters are characteristics of populations
• What is the role of p-values in hypothesis testing?

  • Indicate the probability of observing data under H0
  • Help determine whether to reject H0
• What is the significance of the alpha level in hypothesis testing?

  • Determines the threshold for rejecting H0
  • Commonly set at 0.05 in psychology
• How do Z-scores standardize data in hypothesis testing?

  • Transform scores to have a mean of 0
  • Transform standard deviation to 1