7.5.2.1.1 Levels of Significance

• What is the level of significance in hypothesis testing?

  It is denoted as alpha.
• If the p-value is 0.03 and alpha is 0.05, what should we do?

  Reject the null hypothesis.
• What is the relationship between p-value and alpha in hypothesis testing?

  • If p-value < alpha, reject the null hypothesis.
  • If p-value ≥ alpha, do not reject the null hypothesis.
• What does an alpha of 0.05 signify?

  5% chance of wrongly rejecting null hypothesis
• What does an alpha of 0.01 signify?

  1% chance of error in results
• What conclusion can be drawn if the p-value is less than alpha?

  Results are significant enough to conclude a real effect.
• What does it indicate if the p-value is greater than or equal to alpha?

  We fail to reject the null hypothesis (H₀)
• When does a Type II error occur?

  When the p-value is greater than or equal to alpha
• Why is an alpha of 0.01 considered stricter than 0.05?

  It allows for less chance of error
• What is a Type II error in hypothesis testing?

  Failing to reject the null hypothesis when false
• Why is the level of significance important in hypothesis testing?

  It helps decide whether to reject the null hypothesis.
• How does the level of significance (alpha) affect Type I and Type II errors?

  A lower alpha decreases Type I error risk
• What factors influence the choice of level of significance?

  • Risk of Type I error
  • Risk of Type II error
  • Cost of being wrong
  • Sample size
• How do you set up null and alternative hypotheses?

  Identify what you're comparing and state both possibilities
• What does the level of significance represent in hypothesis testing?

  It represents the probability of making a Type I error.
• What does the null hypothesis represent in a study?

  It represents the default assumption of no effect
• Why is minimizing Type I and Type II errors important in research?

  To improve the accuracy of research conclusions
• What is the alternative hypothesis (H₁)?

  There is a real difference or effect
• What alpha value is commonly used in anxiety treatment effectiveness studies?

  0.05
• Why is the level of significance not a one-size-fits-all decision?

  It depends on several influencing factors.
• What does the alternative hypothesis suggest?

  It suggests that the suspected effect is true
• Why is it important to state both hypotheses clearly?

  It guides data analysis and determines significance
• When does a Type I error occur?

  When the p-value is less than alpha
• When should you use an alpha of 0.05 versus 0.01?

  • Use 0.05 when slight error is acceptable
  • Use 0.01 when large error would be serious
• What should researchers consider when selecting an alpha level?

  Risk tolerance and potential cost of errors
• In what scenario would you choose an alpha of 0.01?

  Testing a new cancer treatment
• What does a p-value represent in hypothesis testing?

  Probability of results by chance if null is true.
• What happens to the risk of Type II error when alpha is lowered?

  The risk of Type II error increases
• What are the consequences of Type I and Type II errors in research?

  • Type I Error:
  • Can lead to unnecessary actions
  • May result in false claims of effectiveness

  
  

  • Type II Error:
  • Can miss important discoveries
  • May result in overlooking effective treatments
• What is the level of significance commonly used in research?

  0.05 and 0.01
• What do we compare to make a decision about the null hypothesis?

  p-value and level of significance (alpha)
• What is the decision-making process based on p-value and alpha?

  • Compare p-value to alpha (level of significance)
  • If p-value < alpha: reject H₀
  • If p-value ≥ alpha: fail to reject H₀
• What is the chance of error associated with an alpha of 0.05?

  5%
• What does it mean if the p-value is less than alpha?

  We reject the null hypothesis (H₀)
• How does a lower alpha affect false positives and false negatives?

  Reduces false positives, increases false negatives
• What is a Type I error in hypothesis testing?

  Rejecting the null hypothesis when it's true
• How does a higher alpha affect false positives and false negatives?

  Reduces false negatives, increases false positives
• What are the definitions and impacts of Type I and Type II errors?

  • Type I Error:
  • Definition: Reject a true null hypothesis
  • Impact: False positive; unnecessary actions
  • Example: Concluding a drug is effective when it's not

  
  

  • Type II Error:
  • Definition: Fail to reject a false null hypothesis
  • Impact: False negative; missed discoveries
  • Example: Failing to detect an effective treatment
• What is the consequence of setting alpha at 0.01 in educational studies?

  Reduces chance of wrongly attributing success
• What is the null hypothesis (H₀)?

  There's no real difference or effect