## Introduction and Contents

We can all look up statistics books, but I decided to include a primer on the subject as a quick reference. I have included the more mathematical parts in red text so they can be skipped, and the principles can still hopefully be understood graphically. The aim is to provide a broad understanding of a few important concepts, such as:

- The difference between normally and non-normally distributed data
- What a p-value means, why 0.05 is considered an important value, and what we mean when we use the term “significant”.
- How to determine if a sample is significantly different from a certain mean value, or if samples are significantly different from one another.
- The difference between saying there is no evidence for a link between x and y and saying there is evidence for no link between x and y.
- Choosing the right statistical test for the right circumstance

The content pages of this primer are listed below. They may be read individually for reference, or in order as a more formal guide:

- Standard Distribution and Standard Error
- Determining the Standard Error of the Mean
- Estimating the True Mean from a Sample Mean – “Significant Differences”
- One-Tailed versus Two-Tailed Probability
- Confidence Intervals
- No Evidence for a Significant Difference vs Evidence for no Significant Difference
- Determining the False Negative Error
- Power Calculations for One Sample
- Statistical Tests on Normally Distributed Data comparing Two Samples
- Standard Errors and Estimation
- Power Calculations for Two Samples
- Comparing a Sample Proportion with an Expected Proportion
- Comparing Two Sample Proportions
- Student’s t-Test for comparing Two Independent Samples
- Paired t-Test for Dependent Samples
- Comparing Variables of Different Types
- Determining and Comparing Times to a Discrete Event: the Kaplan-Meier Survival Plot
- Summary of Statistical Tests
- Designing a Research Study

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