if your study has 80% power, it has an 80% chance of detecting an effect that exists. Let this point be a reminder that when you work with samples, nothing is guaranteed! When an effect exists in the population, your study might not detect it because you are working with a sample. Samples contain sample error, which can occasionally cause a random

The significance (alpha) level—how far out from the null value is the critical region?

P-values indicate the strength of the sample evidence against the null hypothesis. If it is less than the significance level, your results are statistically significant.

Descriptive statistics describe a sample. That’s pretty straightforward. You simply take a group that you’re interested in, record data about the group members, and then use summary statistics and graphs to present the group properties.

You can think of the null as the default theory that requires sufficiently strong evidence in your sample to be able to reject it.

(significance level). Your sample evidence provides sufficient evidence to conclude that the effect exists in the population.

A statistic is a characteristic of a sample. If you collect a sample and calculate the mean and standard deviation, these are sample statistics. Inferential statistics allow you to use sample statistics to make conclusions about a population.

Larger sample sizes allow hypothesis tests to detect smaller effects. If Study B’s sample size is large enough, its more modest effect can be statistically significant.

hypothesis tests make assumptions about the data collection process. For instance, these tests assume that the data were collected using a method that tends to produce representative samples.