"There are three kinds of lies: lies, damned lies, and statistics." - Mark Twain
This lesson assumes that you have taken a basic statistics course and have some familiarity with hypothesis testing. If you have not taken statistics before, it is strongly recommended that you take the free Udacity courses in descriptive statistics and inferential statistics before attempting this lesson. This lesson also assumes that you have beginner Stata skills and can complete the exercises in the lessons on Intro to Stata and Stata Best Practices.
Statistical inference is the basis for all quantitative tools in the IDinsight toolkit, including needs assessment, impact evaluation, process evaluation, and monitoring.
Statistical inference provides a key input in decision-making about programs and policies.
An intuitive understanding of the Central Limit Theorem is critical for diagnosing statistical problems and correctly interpreting the results of statistical tests.
The hypothesis test is the most important tool in the statistical inference toolbox.
The hypothesis test has five main steps. Statistical software will only do two of them for you. (steps 1 & 2)
The hypothesis test has five main steps. Statistical software will only do two of them for you. (steps 3, 4, & 5)
Test a single sample mean against a null hypothesis using data from the EG DIB evaluation.
Test the equality of two sample means using data from the EG DIB evaluation.
Banner photo: William Playfair's bar chart showing the price of wheat over time, an early example of a bar chart. Accessed from https://www.lindahall.org/william-playfair/