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

- 2 distinctions:

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