Statistics measurements can be used to interpret and analyze data. These measurements can be used for describing the distribution of data, identifying trends and making predictions. The basic statistical measurements are measures of central tendencies (e.g. mean, median and mode), dispersion measures (e.g. range, variance and standard deviation) as well as measures of association.

To determine whether a relationship exists between variables, statistical tests can be used. The two types of tests are parametric and not-parametric. Parametric tests are based on the assumption that data follows a normal distribution. The type of data to be analyzed, and the research questions being examined will determine the test that is appropriate.

Validity and reliability are important factors when using sources of data in research. The reliability of a data source is determined by its consistency and accuracy, while the validity measures the degree to with which it accurately measures what the data are meant to measure. The data sources may be invalid because of errors made in their collection, coding or processing. Standardized data collection techniques, clear inclusion/exclusion criteria and quality assurance checks are important for ensuring the validity and reliability of data sources.

In summary, statistics and statistical tests are useful for analysing and interpreting the data. The type of data being examined and the research question will determine which statistical test is appropriate. To ensure accuracy and consistency, it is important to take into account reliability and validity when using research data.