# Fundamentals of Statistics

## 1. Purpose and Fundamentals of Statistics

*(This section is organised in the form of a book, please follow the blue arrows to navigate through the book or by following the navigation panel on the right side of the page.)*

*Data alone is not interesting. It is the
interpretation of the data that we are really interested in…
*

*Jason Brownlee – 2018*

Statistical methods provide formal accounting for sources of variability in patients’ responses to treatment. The use of statistics allows the clinical researcher to form reasonable and accurate inferences from collected information, and sound decisions in the presence of uncertainty. Statistics are key to preventing errors and bias in medical research.

Hypothesis
testing is one of the most important concepts in statistics because it is how
you decide if something really happened, or if certain treatments have positive
effects or are not different from another treatment. Hypothesis testing is
common in statistics as a method of making decisions using data. In other
words, testing a hypothesis is trying to determine if, based on statistics, your
observations are likely to have really occurred. In short, you want to proof if
your data is statistically significant and unlikely to have occurred by chance
alone. In essence then, a hypothesis test is a test of significance.

- Null and alternative hypothesis
- Sample size
- Bias
- Type I error
- Type II error
- Significance
- Power
- Confidence intervals