In this chapter, the fundamental concepts of the contrast of hypotheses will be explored with the aim of identifying the most important elements of this process and recognizing the main variants of interests.

The **p-value** is a statistical measure used to assess the strength of evidence against a null hypothesis in hypothesis testing. It represents the probability of obtaining a result as extreme as, or more extreme than, the observed data under the assumption that the null hypothesis is true.

A repeated comparison occurs when the same type of statistical test or comparison is performed multiple times within a study. This situation arises when comparing several groups or conditions pairwise (for instance, comparing group A vs. group B, group A vs. group C, etc.), each individual comparison is repeated across different pairs. The more comparisons you perform, the greater the chance of observing a statistically significant result purely by chance.

Parametric and non-parametric tests are two major classes of statistical methods used to analyze data, each with distinct assumptions, advantages, and limitations. Their appropriate use depends on the nature of the data and the research questions at hand.

Confidence intervals are fundamental for evaluating the evidence in any study. It is important to understand this concept and its interpretation.

We discuss several examples that help in the interpretation of a confidence interval

Here we present the basic confidence intervals for probabilities and means, including the estimation of differences among two groups. Those are basic tools for evaluating treatment effects.

Relative risk (RR) and Odds Ratios (OR) are important risk measures in epidemiology. Here, we define these indexes and show their interpretation.