Ranstam

One form of selection bias that is linked with information bias is called regression-to-the-mean (RTM). The phenomenon occurs in groups of people that are selected because they are abnormal, and it shows up as a tendency to return to normality. For example, if people with abnormally high blood pressure are selected to follow up, these patients' average blood pressure will appear lower the next time the blood pressure is measured. The apparent change in mean blood pressure is a statistical phenom...

Selection bias is a systematic error that occurs when the sample selected for a study does not accurately represent the population. This problem can occur in many ways, often referred to by different names. For example, when recruiting subjects for a follow-up study of health hazards in certain workplaces, comparisons of illnesses and deaths are usually made with the whole population, adjusted for age and sex. However, some subjects may be excluded from the workforce because of diseases or acci...

The accuracy of measurements and statistical estimates has two components, precision and validity. For example, a pistol shooter trying to hit the bull's-eye may shake his hand when firing (inaccuracy) or have the rear sight poorly set (systematic error). P-values, confidence intervals and statistical significance are the precision measures used in statistical inference. Systematic errors (validity problems) are known as bias. The statistical precision of a hypothesis test or effect size estim...

The difference between facts and beliefs is that facts are based on objective evidence and can be verified or disproved through experimentation, observation and logical reasoning, i.e. evidence. Proof is evidence that is considered so conclusive that it establishes a fact beyond reasonable doubt. Faith and dogma, rather than evidence, are based on beliefs that may or may not reflect a commitment to certain moral, religious or political principles. However, whereas faith is personal and open to ...

The statistical section of research reports often includes information on how the distributional properties of the data have been assessed. Summaries of non-normally distributed variables are then presented using median and min-max instead of mean and standard deviation, and hypothesis testing is done using distribution-free tests instead of asymptotic tests. Several tests have been developed to test hypotheses about the distributional properties of a variable, such as the Kolmogorov-Smirnov tes...

This note addresses some important aspects of meta-analysis that are often overlooked. Observational studies differ from randomised trials in the respect that an observational study cannot be designed to prevent validity problems by randomisation, concealed allocation, and masking. The statistical analysis needs to be based on special considerations regarding internal validity and include adjustments to reduce bias. How well these issues have been addressed needs to be considered in detail an...

Multiple regression analysis is often used in statistical analyses involving multiple variables to fit statistical models. Their use is often problematic, both terminologically (as discussed here: 2. Terminology) and in terms of the purpose of the analysis. The British statistician George Box coined the phrase, "All models are wrong, but some are useful". In clinical and epidemiological research, three main uses are common. First, in observational studies, multiple variables are included in a s...

As indicated in another post, statistical significance and p-values are often misunderstood, and this is not a new problem. Frank Yates, one of the pioneers of 20th century statistics, stated as early as 1951 that the most common weakness is the failure to recognise that estimates of treatment effects, together with estimates of the errors to which they are subject, are the quantities of primary interest in clinical research, not p-values (Yates F. The influence of Statistical Methods for Resear...

A common but flawed view of medical research is that all you need is a data set and the ability to run statistical tests. In the past, when tests had to be calculated by hand or on a mainframe, it took statisticians to do the tests. Today, with personal computers and easy-to-use software, anyone can calculate p-values. A p-value is the probability of drawing a sample with a characteristic that is at least as extreme as a certain value (for example, an apparent effect observed in a particular sa...

"How do we know that cigarettes cause lung cancer?" the professor asked, going on to say, "It has never been tested in a clinical trial. The implication was that a clinical trial was needed to know for sure, and that observational studies could at best provide suggestions for further research. There was no discussion of the ethical and practical problems of conducting clinical trials to assess harmful effects on participants. Clinical trials, unlike observational studies, can be designed to r...

created_at: 2025-02-21 17:08:05 Michael Healy, professor of medical statistics at the London School of Hygiene and Tropical Medicine, described clinical research in a Fisher Memorial Lecture in 1995 as "a largely amateur pursuit conducted by doctors". Whether or not this statement is true today, misuse of well-defined statistical terms is a certain way of appearing amateurish. This problem can easily be avoided by checking the definitions of the terms used. I recommend consulting The Oxford...