What is the purpose of hypothesis testing in risk modeling?

The goal of hypothesis testing in risk modeling is to evaluate the validity of a proposed statement about a population parameter, usually through the framework of the null hypothesis and an alternative hypothesis. The focus is on gathering sufficient evidence to determine whether to reject the null hypothesis, which represents a default position that indicates no effect or no difference.

In this context, when statistical tests are conducted, they provide a systematic way to evaluate the data and ascertain whether the observed effects can be attributed to random chance or if they reflect a true effect in the population. If the evidence from the data is strong enough, it leads to the decision to reject the null hypothesis in favor of the alternative hypothesis, which signifies that there may be a significant relationship or effect worth further investigation.

While determining whether the sample mean equals the population mean is one facet of hypothesis testing, it does not encompass the broader objective of the process, which aims at evaluating evidence against the null hypothesis. Similarly, establishing a causal relationship between variables is often outside the scope of standard hypothesis testing, as it typically does not directly address causality without further experimental design. Proving a theory beyond doubt is not feasible within the realm of statistical inference, as all scientific inquiry remains subject to uncertainty and varying degrees of evidence.