Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

Specifically, alpha represents the probability that tests will produce statistically significant results when the null hypothesis is correct. You can think of this error rate as the probability of a false positive.

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

are the probability that a sample will have an effect at least as extreme as the effect observed in your sample if the null hypothesis is correct.

To represent a significance level of 0.05, I’ll shade 5% of the distribution furthest from the null value.

to make inferences about populations, you need statistical methods that incorporate estimates of sampling error.

the tradeoff for working with a manageable sample requires that we account for sample error.

when you estimate the properties of a population from a sample, the sample statistics are unlikely to equal the actual population value exactly.

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.

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.