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Updated: Sep 15 2019

Statistical Hypotheses and Error

  • Hypotheses
    • Null Hypothesis (H0)
      • Hypothesis of no difference
        • i.e. There is no link between disease and risk factor
    • Alternative Hypothesis (H1)
      • Hypothesis of difference
        • i.e. There is a link between disease and risk factor
  • Type I error (False positive)
    • Stating there is an association when none exits
      • Incorrectly rejecting null hypothesis
    • α = probability of type I error
    • p = probability that results as or more extreme than those of the study would be observed if the null hypothesis were true 
      • General rule of thumb is that statistical significance is reached if p ≤ 0.05
  • Type II error (False negative)
    • Stating there is no effect when an effect exists
      • Incorrectly accepting null hypothesis
    • β = probability of type II error
  • Power (True Positive)
    • Probability of correctly rejecting null hypothesis
      • Power = 1 - β
    • Power depends on
      • Sample size
        • Increasing sample size increases power
      • Size of expected effect
        • Increasing effect size increases power
  • True Negative
    • Probability of correctly accepting null hypothesis
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