p-value use to determine if there’s a “statistically significant difference” between groups of data probability of error by convention, if p > 0.05, then “there is no effect ” if p < 0.05, then “there is an effect” The remaining slides (optional) provide a more in-depth description of the p-value. Click voice clip

p-value (probability of a type I error). null hypothesis (H o ) = “there is no effect”. H o is accepted, unless there is good evidence to reject it. accept H o reject H o H o is validcorrect decisiontype I error H o is invalidtype II errorcorrect decision Table 1. Types of errors in a statistical test

Determine the value of the p-value it’s a calculus / numerical methods problem example: Probability of the test statistic used in the t-test, which is used to compare 2 groups of data. is the area under the graph that describes the probability of getting a specific value of the test statistic.

. Types of p-values 2-tail p-values null hypothesis: X 1 = X 2 “there is no effect; the data is the same” alternative hypothesis: X 1 ≠ X 2 “there is an effect; the data is different”

1-tail p-value null hypothesis: X 1 ≤ X 2 alternative hypothesis: X 1 > X 2

1-tail p-value null hypothesis: X 1 ≥ X 2 alternative hypothesis: X 1 < X 2