Common pitfalls in statistical analysis: Clinical versus statistical significance

Common pitfalls in statistical analysis: Logistic regression

Logistic regression analysis is a statistical technique to evaluate the relationship between various predictor variables (either categorical or continuous) and an outcome which is binary (dichotomous). In this article, we discuss logistic regression analysis and the limitations of this technique.

Common Pitfalls in Statistical Thinking

works for quality management and process improvement, statistical thinking is an intrinsic part of building organizational capability. In all of these frameworks there is a degree of flexibility in how the statistical thinking concepts are implemented, because these frameworks apply to many different contexts. Flexibility, however, also leads to ambiguity and inconsistency. The purpose of this ...

Statistical Significance versus Clinical Significance in Clinical Trials

Common pitfalls in statistical analysis: Linear regression analysis

In a previous article in this series, we explained correlation analysis which describes the strength of relationship between two continuous variables. In this article, we deal with linear regression analysis which predicts the value of one continuous variable from another. We also discuss the assumptions and pitfalls associated with this analysis.

Common Statistical Pitfalls in Basic Science Research

T he analysis of clinical samples, population samples, and controlled trials is typically subjected to rigorous statistical review. This fact is understandable, given that the results of clinical investigation will often be used to inform patient care or clinical decision making. One would not want to predicate patient advice on research findings that are not correctly interpreted or valid. For...