Phew! Nobody said hypothesis testing is easy, but you made it to the end of the lesson. Congratulations! The world of hypothesis testing is vast. There is much more you can learn, and so many applications where you can use them.
Let’s review what you’ve learned in this lesson:
- Samples are subsets of an entire population, and the sample mean can be used to approximate the population mean
- The null hypothesis is an assumption that there is no difference between the populations you are comparing in a hypothesis test
- Type I Errors occur when a hypothesis test finds a correlation between things that are not related, and Type II Errors occur when a hypothesis test fails to find a correlation between things that are actually related
- P-Values indicate the probability that, assuming the null hypothesis is true, such differences in the samples you are comparing would exist
- The Significance Level is a threshold p-value for which all p-values below it will result in rejecting the null hypothesis
- One Sample T-Tests indicate whether a dataset belongs to a distribution with a given mean
- Two Sample T-Tests indicate whether there is a significant difference between two datasets
- ANOVA (Analysis of Variance) allows you to detect if there is a significant difference between one of multiple datasets
Review your code from the previous exercise. Try running a One Sample T-Test or a Two Sample T-Test on on some of the data. What results do you find?