A set of statistical procedures to test hypotheses (i.e., make inferences) about a population
Null Hypothesis Significance Testing (NHST)
The process by which researchers determine if their data support or fail to support their hypothesis
NHST
1. State hypotheses
2. Set the criterion for burden of proof (alpha level)
3. Collect data and calculate statistics
4. Make a decision about the null hypothesis according to the criterion from Step 2
Null Hypothesis (Ho)
States that there is no change, no difference, no relationship
Alternative Hypothesis (H1)
States that there is a change, a difference, or a relationship
Alpha level (α)
The probability value criterion that is used to distinguish very unlikely sample means from likely sample means
Test statistic
Any statistic with a known distribution from which we can calculate a p-value (e.g., z, t, F)
value
The probability of obtaining a test statistic that is that large or larger if the null hypothesis is true
The equation for a z-test is: z = (x_i - μ) / σ
Type I error
Occur when we reject the null hypothesis when it is in fact true
Type II error
Occur when we fail to reject the null hypothesis when the null hypothesis is really false
To reduce Type I errors, lower the alpha level so that the cut-off is more stringent
To reduce Type II errors, lower measurement error, collect data more precisely, increase the size or detectability of thing you are trying to measure, collect a large sample
