Null Hypothesis Significance Testing (p value approach)

  • p value is the probability of observing data as extreme (or more) as observed assuming that the null hypothesis is true
  • should be used less, should be made clear that they are of limited value
  • a statistically significant result is one for which chance is an unlikely explanation

Effect sizes

  • Effect sizes tell the reader how big the effect is (something p value doesn’t do)
  • import to report the units of measurement of the effect size
    • 2 distinctions:
      • effect can be reported in units of the original variables, or in standardized units (mean on a test is 3 correct answers higher in one group than in another, vs one group scores one standard deviation higher than another)
      • between effects for the differences between group means and effects in terms of proportion of variation or association

Causal and associative hypotheses

  • Causal
    • implies that changing some aspect of the environment will tend to create some difference
    • to have this, it’s necessary to think about manipulating some aspect of the system
  • Associative
    • describes how variables relate to each other in the absence of manipulation
    • sampling is critical for investigating associative hypotheses

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