Hypothesis Testing: An Intuitive Guide for Making Data Driven Decisions

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

When you can reject the null hypothesis, the results are statistically significant, and your data support the theory that an effect exists at the population level.

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

The Type II error rate (beta) is the probability of a false negative. Therefore, the inverse of Type II errors is the probability of correctly detecting an effect. Statisticians refer to this concept as the power of a hypothesis test. Consequently, 1 – β = the statistical power. Analysts typically estimate power rather than beta directly.

Typically, you do not know the size of the actual effect. However, you can use a hypothesis test to determine whether an effect exists and estimate its size.

Power analysis helps you manage an essential tradeoff. As you increase the sample size, the hypothesis test gains a greater ability to detect small effects. This situation sounds fantastic. However, larger sample sizes cost more money. And, there is a point where an effect becomes so miniscule that it is meaningless in a practical sense.

there are multiple reasons for Type II errors—small effect sizes, small sample sizes, and high data variability.

hypothesis testing builds on a broad range of statistical knowledge, such as inferential statistics, experimental design, measures of central tendency and variability, data types, and probability distributions

The probability of making a Type II error is known as beta (β).