Frequentistisissä

Frequentist statistics is a branch of statistics that focuses on the long-run frequency of events. It is one of the two main approaches to statistical inference, the other being Bayesian statistics. The frequentist approach is based on the idea that the probability of an event is the limit of its relative frequency in a large number of trials. This means that probabilities are seen as objective properties of the world, independent of any particular observer or set of observations.

In frequentist statistics, parameters are treated as fixed but unknown quantities. The goal of statistical inference is to estimate these parameters based on sample data. This is typically done using point estimates, which are single values that are used to estimate the parameter, and confidence intervals, which provide a range of values within which the parameter is expected to lie with a certain level of confidence.

Frequentist methods include hypothesis testing, where the goal is to determine whether a hypothesis about a population parameter is true or false, and p-values, which are used to quantify the strength of evidence against a null hypothesis. These methods are widely used in many fields, including science, engineering, and business, due to their simplicity and ease of interpretation.

However, frequentist statistics has been criticized for its reliance on arbitrary choices, such as the choice of significance level in hypothesis testing, and for its inability to incorporate prior knowledge or beliefs into the analysis. These criticisms have led to the development of Bayesian statistics, which treats probabilities as subjective degrees of belief and allows for the incorporation of prior information. Despite these criticisms, frequentist statistics remains a widely used and important approach to statistical inference.