Hypoteesitestien

Hypoteesitestien, commonly referred to as hypothesis testing in English, is a fundamental statistical method used to make decisions or draw conclusions about a population based on sample data. It involves formulating a null hypothesis, denoted as H0, which represents a statement of no effect or no difference, and an alternative hypothesis, denoted as H1 or Ha, which represents the claim being investigated, suggesting an effect or difference.

The process begins with collecting sample data. This data is then used to calculate a test statistic, which quantifies the difference between the observed data and what would be expected under the null hypothesis. Associated with this test statistic is a p-value, which represents the probability of observing data as extreme as, or more extreme than, the collected sample, assuming the null hypothesis is true.

A predetermined significance level, often denoted as alpha (α), is set before conducting the test. This alpha level represents the threshold for rejecting the null hypothesis. Common alpha values are 0.05 or 0.01. If the calculated p-value is less than or equal to the significance level (p ≤ α), the null hypothesis is rejected in favor of the alternative hypothesis. This suggests that the observed data provides sufficient evidence to support the claim made in the alternative hypothesis.

Conversely, if the p-value is greater than the significance level (p > α), the null hypothesis is not rejected. This does not necessarily mean the null hypothesis is true, but rather that the sample data does not provide enough evidence to reject it at the chosen significance level. Hypothesis testing is a crucial tool in various fields, including scientific research, business analytics, and social sciences, for validating theories and making data-driven decisions.