Okay somehow you managed to survive to this point. A few month ago, you were lamenting about your intro to stats course and how you would survive it. But you are still here. I am referring to your introductory statistics course. It’s towards end of the Spring semester. Final Exams are around the corner. How should you study for it? I have taught many introductory courses in statistics. Here are a few common themes I have come across in terms of a introductory statistics final. The list is as follows:

- Probability calculations with the z and t tables. You know how to derive a probability using the mean and standard deviation (for z tables) or standard error (for t tables). You should understand the area under the curve. Real talk: make sure you know what area that probability represents so you can manipulate it. If you need the probability of z greater than a value (P z>Z), you need to subtract 1 from your z table probability. Study what information is necessary in order to use a particular table.
- Regression: know how to determine a variable is significant to a model. You should understand what the beta and intercept represent in terms of your outcome variables.
- Central limit theorem and when it applies.
- How to normalize a distribution that is not normal (example logistic regression)
- Know how to construct the standard deviation from scratch. Yeah I know it’s a pain. I suggest you pick a few numbers like 5,10,13,20,4 and go through the mechanics f it (answer: 5.82 if you are talking about the population or 6.5 if you are referring to a sample population)
- Terminology: For instance, population and sample have different equations for standard deviation/error. sample has n-1 in the denominator…know the difference.
- Confidence intervals: know how to calculate them….like really meditate on what information is necessary to get a confidence interval. And know how to interpret one. That way no one can trick you , especially on a multiple choice exam.
- Know which statistical tests are used for a specific variable type. Dichotomous outcome variables are analyzed in logistic regression, Fisher exact, and Chi Square tests. Know the test and heck be the test. Know it like the back of your hand.
- Master how to set up hypothesis tests.It’s the kind of thing you can’t fake it. You either know it or you don’t. And there are hypothesis tests for every type of outcome variable. Don’t just focus on the z-tables. Throw in some chi-square test for independence in there.
- Bayes formula and conditional probabilities: This is slightly different from #1. The teacher wants to know if you can derive probabilities from specific scenarios. Don’t beat yourself up if this is difficult for you.
- Special mention: know how to read statistical output from a regression analysis in SPSS ,SAS, or R. Your homework assignment probability had some exercise where you had to interpret a table or graph. Just know what the numbers mean in the illustration.
- How to generate descriptive by hand like the mean, median, inter-quartile range, and the mode. What they mean and when to use them for a data summary (has to do with skewness).

Okay so there you have it. A few main concepts which pop-up in any introductory course for statistics. if you enjoyed this blog there are more to follow-Amy

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