The evaluation of diagnostic tests before approval for clinical practice is another important area of epidemiological study. Variables can furthermore be categorized qualitatively in categorical terms (e.g., eye color, sex, race) and quantitatively in numerical terms (e.g., age, weight, temperature). Variables can be independent, in the sense that they are not dependent on other variables and can thus be manipulated by the researcher for the purpose of a study (e.g., administration of a certain drug), or dependent, in the sense that their value depends on another variable and, thus, cannot be manipulated by the researcher (e.g., a condition caused by a certain drug). The values used to describe features of a sample or data set are called variables. Inferential statistics involves parameters such as sensitivity, specificity, positive/ negative predictive values, confidence intervals, and hypothesis testing. The counterpart of descriptive statistics, inferential statistics, relies on data to make inferences that do go beyond the scope of the data collected and the sample from which it was obtained. Measures of dispersion describe how data is distributed and include range, quartiles, variance, and deviation. Measures of central tendency describe the central distribution of data and include the mode, median, and mean. Common measures of descriptive statistics are those of central tendency and dispersion. Descriptive statistics measure, describe, and summarize features of a collection of data/sample without making inferences that go beyond the scope of that collection/sample. At the same time, flaws in study design can affect statistics and lead to incorrect conclusions. Statistics is the science of collecting, analyzing, and interpreting data, and a good epidemiological study depends on statistical methods being employed correctly. Statistical analysis is one of the principal tools employed in epidemiology, which is primarily concerned with the study of health and disease in populations.