Statistical tests are essential tools in research used to analyze data and determine the significance of findings.
They allow researchers to assess whether the observed results are likely due to chance or represent genuine relationships within the studied population.
By applying appropriate tests, investigators can make evidence-based decisions and draw valid conclusions from sample data.

There are various types of statistical tests, each suited for specific research designs and data characteristics.
For example, t-tests are often used to compare means between two groups, while ANOVA is applied when analyzing differences across multiple groups.
Non-parametric tests, such as the chi-square test, are employed when data do not meet the assumptions of parametric methods, ensuring robust and reliable results.

Choosing the correct statistical test involves considering factors such as sample size, data distribution, and research objectives.
Proper application of these tests not only improves the accuracy of the analysis but also minimizes the risk of error or bias. As a result, statistical testing plays a critical role in supporting scientific rigor and advancing knowledge in virtually every field of study.