Hypoteesitestauksella

Hypoteesitestaus, also known as hypothesis testing, is a fundamental statistical method used to make decisions or draw conclusions about a population based on sample data. It involves formulating a specific hypothesis about a population parameter and then using statistical evidence to determine whether the data supports or refutes that hypothesis.

The process begins with defining two competing hypotheses: the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis typically represents the status quo or a statement of no effect or no difference. The alternative hypothesis, on the other hand, proposes that there is an effect, a difference, or a relationship.

Next, a significance level (alpha), denoted as α, is chosen. This value represents the probability of rejecting the null hypothesis when it is actually true, also known as a Type I error. Common alpha values are 0.05 (5%) or 0.01 (1%).

A test statistic is calculated from the sample data. This statistic measures how far the sample result deviates from what would be expected under the null hypothesis. The calculated test statistic is then compared to a critical value or used to determine a p-value.

The p-value is the probability of observing sample data as extreme as, or more extreme than, the observed data, assuming the null hypothesis is true. If the p-value is less than or equal to the chosen significance level (p ≤ α), the null hypothesis is rejected in favor of the alternative hypothesis. If the p-value is greater than the significance level (p > α), there is not enough evidence to reject the null hypothesis.

Hypothesis testing is widely used in various fields, including science, business, and social sciences, to validate theories, assess the effectiveness of interventions, and make data-driven decisions.