1. Summary of statistical inference
Throughout the course,
2. Inference
we have focused on making claims about a population using information from a sample of data.
3. Testing
Hypothesis testing is used to test a particular claim about the population.
Remember, the claim to refute is called the null hypothesis, while the claim you want is called the alternative hypothesis. For example, in the gender discrimination example, the null hypothesis is that there is no gender discrimination in hiring. The alternative hypothesis is that men are more likely to be promoted than women.
These ideas are important to remember because the entire process of hypothesis testing will be repeated with different types of data and research questions in later courses.
4. Estimation
Estimation is used to understand the value of a population parameter.
Using measures of variability of the statistic, we can estimate how far p-hat is from the true proportion.
5. Bootstrapping
In particular, the variability of p-hat can be measured using resamples from the original data. This process is called bootstrapping.
As with hypothesis testing, the entire bootstrap process will be repeated with different data structures and research questions in later courses.
6. Congratulations!
Thanks for taking this course on statistical inference with me. I look forward to seeing you in future courses.