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Medical statistics (also health statistics) deals with applications of statistics to medicine and the health sciences, including epidemiology, public health, forensic medicine, and clinical research.[1] Medical statistics has been a recognized branch of statistics in the United Kingdom for more than 40 years, but the term has not come into general use in North America, where the wider term 'biostatistics' is more commonly used.[2] However, "biostatistics" more commonly connotes all applications of statistics to biology.[2] Medical statistics is a subdiscipline of statistics.

It is the science of summarizing, collecting, presenting and interpreting data in medical practice, and using them to estimate the magnitude of associations and test hypotheses. It has a central role in medical investigations. It not only provides a way of organizing information on a wider and more formal basis than relying on the exchange of anecdotes and personal experience, but also takes into account the intrinsic variation inherent in most biological processes.[3]

Use in medical hypothesis testing

In medical hypothesis testing, the medical research is often evaluated by means of the confidence interval, the P value, or both.[4]

Confidence interval

This probability distribution presents two different confidence intervals.[1]

Frequently reported in medical research studies is the confidence interval (CI), which indicates the consistency and variability of the medical results of repeated medical trials. In other words, the confidence interval shows the range of values where the expected true estimate would exist within this specific range, if the study was performed many times.[1]

Most biomedical research is not able to use a total population for a study. Instead, samples of the total population are what are often used for a study. From the sample, inferences can be made of the total population by means of a sample statistic and the estimation of error, presented as a range of values.[1][4]

P value

Frequently used in medical studies is the statistical significance of P < 0.05.[4]

The P value is the probability of no effect or no difference (null hypothesis) of obtaining a result essentially equal to what was actually observed. The P stands for probability and measures how likely it is that any observed difference between groups is due to chance. The P value function between 0 and 1. The closer to 0, the less likely the results are due to chance. The closer to 1, the higher the probability that the results are actually due to chance.[4]

Pharmaceutical statistics

Pharmaceutical statistics is the application of statistics to matters concerning the pharmaceutical industry. This can be from issues of design of experiments, to analysis of drug trials, to issues of commercialization of a medicine.[1]

There are many professional bodies concerned with this field including:

Clinical biostatistics

Clinical biostatistics is concerned with research into the principles and methodology used in the design and analysis of clinical research and to apply statistical theory to clinical medicine.[1][5]

Clinical biostatistics is taught in postgraduate biostatistical and applied statistical degrees, for example as part of the BCA Master of Biostatistics program in Australia.

Basic concepts

For describing situations
For assessing the effectiveness of an intervention

Related statistical theory

See also


  1. ^ a b c d e f "Finding and Using Health Statistics". US National Library of Medicine. Retrieved 2024-03-15.
  2. ^ a b Dodge, Y. (2003) The Oxford Dictionary of Statistical Terms, OUP. ISBN 0-19-850994-4
  3. ^ Kirkwood, Betty R. (2003). Essential Medical Statistics. Malden, Massachusetts: Blackwell. ISBN 978-0-86542-871-3.
  4. ^ a b c d Shreffler, Jacob; Huecker, Martin R. (2024). "Hypothesis Testing, P Values, Confidence Intervals, and Significance". StatPearls. Treasure Island (FL): StatPearls Publishing. PMID 32491353. Retrieved 2024-03-16.
  5. ^ "Our aims". International Society For Clinical Biostatistics. 22 February 2015. Retrieved 15 March 2024.
  6. ^ S, KATZ; FORD A B; MOSKOWITZ R W; JACKSON B A; JAFFE M W (1963). "Studies of Illness in the Aged". Journal of the American Medical Association. 185 (12): 914–9. doi:10.1001/jama.1963.03060120024016. PMID 14044222.
  7. ^ Benjamin, Bernard (1993). The analysis of mortality and other actuarial statistics. England, Institute of Actuaries: Oxford. ISBN 0521077494.

Further reading