Clinical decision making while making a diagnosis is based on pre-test probability evaluation, a decision whether further tests are needed, and post-test probability evaluation based on test results. The add value of the test is the increase in probability, which depends on its diagnostic accuracy. Some measures of diagnostic accuracy are sensitivity, specificity, positive predictive value, negative predictive value, likelihood ratio, area under the curve, Youden index, and diagnostic odds ratio. ROC curve is the most efficient way of graphic description of the relationship between sensitivity and specificity. Diagnostic accuracy of a test is evaluated in a cross-sectional study on a group of individuals subjected simultaneously to the index test and the gold standard. All parameters of diagnostic accuracy are very sensitive to the study design, therefore it is of utmost importance to perform and write a study to adhere to the proposed STARD standards. This lecture gives an insight into the basics of biostatistical terminology from the field of diagnostic accuracy.