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Type I & Type II Errors quiz

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Terms in this set (15)

  • What is a Type I error in hypothesis testing?
    A Type I error occurs when a true null hypothesis is wrongly rejected.
  • What is a Type II error in hypothesis testing?
    A Type II error happens when a false null hypothesis is not rejected.
  • What does the alpha (α) level represent in hypothesis testing?
    Alpha is the threshold for significance and represents the probability of making a Type I error.
  • How can you reduce the probability of a Type I error?
    You can reduce the probability of a Type I error by decreasing the alpha level.
  • What is the probability of making a Type II error called?
    The probability of making a Type II error is called beta (β).
  • How does increasing alpha affect Type II error probability?
    Increasing alpha decreases the probability of a Type II error.
  • What is the relationship between reducing Type I and Type II errors?
    Reducing the chance of one type of error increases the chance of the other type.
  • What does it mean to 'fail to reject' the null hypothesis?
    Failing to reject the null hypothesis means there is not enough evidence to conclude it is false.
  • In the blood pressure treatment example, what is the null hypothesis?
    The null hypothesis is that the mean blood pressure is 120, meaning the treatment works as advertised.
  • What mnemonic was suggested to remember Type I and Type II errors?
    The mnemonic 'rat fluff' helps remember: Type I is Rejecting A True null (RAT), and Type II is Failing to reject a False null (FLUFF).
  • Why is it important to consider the seriousness of Type I and Type II errors?
    Because the consequences of each error type can differ, and the more serious one should be minimized based on context.
  • What does a Type I error mean in the context of the treatment example?
    It means concluding the treatment does not work when it actually does.
  • What does a Type II error mean in the context of the treatment example?
    It means concluding the treatment works when it actually does not.
  • How is the p-value used in relation to alpha in hypothesis testing?
    The p-value is compared to alpha to decide whether to reject the null hypothesis.
  • Why can't beta (Type II error probability) be calculated as 1 minus alpha?
    Because beta is not simply 1 minus alpha; it depends on other factors like sample size and effect size.