What is the objective of hypothesis testing in statistics?

The objective of hypothesis testing in statistics is to assess the validity of pre-defined claims about data. This process involves formulating a null hypothesis, which represents a default position that there is no effect or no difference, and an alternative hypothesis, which signifies a claim that there is an effect or a difference. By using sample data, hypothesis testing allows researchers to use statistical methods to determine whether there is sufficient evidence to reject the null hypothesis in favor of the alternative hypothesis.

Essentially, hypothesis testing helps in making inferences about a population based on sample data, allowing researchers to draw conclusions regarding the claims they have made about the relationship or effects observed in the data. This is a critical aspect of research because it provides a formal mechanism to evaluate the strength of evidence before making decisions or drawing conclusions based on data.

The other options focus on different aspects of statistics. Determining if the data is normally distributed relates to checking assumptions for certain tests rather than making claims about the data. Estimating population parameters is more about deriving values (like means or proportions) from sample data rather than validating specific claims. Calculating effect sizes, while important for understanding the magnitude of relationships or differences, does not address the core goal of testing the validity of a hypothesis.