Bias refers to a systematic error in a research or statistical study. The first large category of bias we will look at is selection bias. Selection bias occurs when those selected for a study are not properly selected or when they are not all retained in a cohort study. The following are subtypes of this bias. Sampling bias also called ascertainment bias occurs when the study population is different from the target population because during selection of subjects there was no randomization. Remember, we can not put the entire population into a study, so the sample we select should be as representative as possible. To do this, we can use…

Part 1: The formulas Part 2: Explanation of the formulas Formulas & Tables Disease + Disease – Test + true positive (TP)a False negative (FN)b a+b PPV = a/(a+b) Test – False positive (FP)c True negative (TN)d c+d NPV = d/(c+d) a+c b+d total SN = a/(a+c) SP = d/(b+d) For Diagnostic tests Formula Sensitivity, SN (true-positive rate) = TP/ (TP +FN) which is a/(a+c) Specificity, SP (true-negative rate) = TN/ (TN + FP) which is d/(b+d) Positive predictive value (PPV) = TP / (TP + FP), which is a/(a+b) Negative predictive value (NPV) = TN / (TN + FN), which is d/(c+d) Incidence rate = # of new cases…