Anthropological survey paper from 1961 by Juhan Aul (et) from University of Tartu who measured about 50 000 people

In fields such as epidemiology, social sciences, psychology and statistics, an observational study draws inferences from a sample to a population where the independent variable is not under the control of the researcher because of ethical concerns or logistical constraints. One common observational study is about the possible effect of a treatment on subjects, where the assignment of subjects into a treated group versus a control group is outside the control of the investigator.[1][2] This is in contrast with experiments, such as randomized controlled trials, where each subject is randomly assigned to a treated group or a control group. Observational studies, for lacking an assignment mechanism, naturally present difficulties for inferential analysis.


The independent variable may be beyond the control of the investigator for a variety of reasons:


Degree of usefulness and reliability

"Although observational studies cannot be used to make definitive statements of fact about the "safety, efficacy, or effectiveness" of a practice, they can:[4]

  1. provide information on 'real world' use and practice;
  2. detect signals about the benefits and risks of...[the] use [of practices] in the general population;
  3. help formulate hypotheses to be tested in subsequent experiments;
  4. provide part of the community-level data needed to design more informative pragmatic clinical trials; and
  5. inform clinical practice."[4]

Bias and compensating methods

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In all of those cases, if a randomized experiment cannot be carried out, the alternative line of investigation suffers from the problem that the decision of which subjects receive the treatment is not entirely random and thus is a potential source of bias. A major challenge in conducting observational studies is to draw inferences that are acceptably free from influences by overt biases, as well as to assess the influence of potential hidden biases. The following are a non-exhaustive set of problems especially common in observational studies.

Matching techniques bias

In lieu of experimental control, multivariate statistical techniques allow the approximation of experimental control with statistical control by using matching methods. Matching methods account for the influences of observed factors that might influence a cause-and-effect relationship. In healthcare and the social sciences, investigators may use matching to compare units that nonrandomly received the treatment and control. One common approach is to use propensity score matching in order to reduce confounding,[5] although this has recently come under criticism for exacerbating the very problems it seeks to solve.[6]

Multiple comparison bias

Multiple comparison bias can occur when several hypotheses are tested at the same time. As the number of recorded factors increases, the likelihood increases that at least one of the recorded factors will be highly correlated with the data output simply by chance.[7]

Omitted variable bias

An observer of an uncontrolled experiment (or process) records potential factors and the data output: the goal is to determine the effects of the factors. Sometimes the recorded factors may not be directly causing the differences in the output. There may be more important factors which were not recorded but are, in fact, causal. Also, recorded or unrecorded factors may be correlated which may yield incorrect conclusions.[8]

Selection bias

Another difficulty with observational studies is that researchers may themselves be biased in their observational skills. This would allow for researchers to (either consciously or unconsciously) seek out the information they're looking for while conducting their research. For example, researchers may exaggerate the effect of one variable, or downplay the effect of another: researchers may even select in subjects that fit their conclusions. This selection bias can happen at any stage of the research process. This introduces bias into the data where certain variables are systematically incorrectly measured.[9]


A 2014 (updated in 2024) Cochrane review concluded that observational studies produce results similar to those conducted as randomized controlled trials.[10] The review reported little evidence for significant effect differences between observational studies and randomized controlled trials, regardless of design.[10] Differences need to be evaluated by looking at population, comparator, heterogeneity, and outcomes.[10]

See also


  1. ^ "Observational study". Archived from the original on 2016-04-27. Retrieved 2008-06-25.
  2. ^ Porta M, ed. (2008). A Dictionary of Epidemiology (5th ed.). New York: Oxford University Press. ISBN 9780195314496.
  3. ^ Kennedy-Martin T, Curtis S, Faries D, Robinson S, Johnston J (November 2015). "A literature review on the representativeness of randomized controlled trial samples and implications for the external validity of trial results". Trials. 16 (1): 495. doi:10.1186/s13063-015-1023-4. PMC 4632358. PMID 26530985.
  4. ^ a b "Although observational studies cannot provide definitive evidence of safety, efficacy, or effectiveness, they can: 1) provide information on "real world" use and practice; 2) detect signals about the benefits and risks of complementary therapies use in the general population; 3) help formulate hypotheses to be tested in subsequent experiments; 4) provide part of the community-level data needed to design more informative pragmatic clinical trials; and 5) inform clinical practice." "Observational Studies and Secondary Data Analyses To Assess Outcomes in Complementary and Integrative Health Care." Archived 2019-09-29 at the Wayback Machine Richard Nahin, Ph.D., M.P.H., Senior Advisor for Scientific Coordination and Outreach, National Center for Complementary and Integrative Health, June 25, 2012
  5. ^ Rosenbaum, Paul R. 2009. Design of Observational Studies. New York: Springer.
  6. ^ King, Gary; Nielsen, Richard (2019-05-07). "Why Propensity Scores Should Not Be Used for Matching". Political Analysis. 27 (4): 435–454. doi:10.1017/pan.2019.11. hdl:1721.1/128459. ISSN 1047-1987. S2CID 53585283. | link to the full article (from the author's homepage
  7. ^ Benjamini, Yoav (2010). "Simultaneous and selective inference: Current successes and future challenges". Biometrical Journal. 52 (6): 708–721. doi:10.1002/bimj.200900299. PMID 21154895. S2CID 8806192.
  8. ^ "Introductory Econometrics Chapter 18: Omitted Variable Bias". Retrieved 2022-07-16.
  9. ^ Hammer, Gaël P; du Prel, Jean-Baptist; Blettner, Maria (2009-10-01). "Avoiding Bias in Observational Studies". Deutsches Ärzteblatt International. 106 (41): 664–668. doi:10.3238/arztebl.2009.0664. ISSN 1866-0452. PMC 2780010. PMID 19946431.
  10. ^ a b c Toews, Ingrid; Anglemyer, Andrew; Nyirenda, John Lz; Alsaid, Dima; Balduzzi, Sara; Grummich, Kathrin; Schwingshackl, Lukas; Bero, Lisa (2024-01-04). "Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials: a meta-epidemiological study". The Cochrane Database of Systematic Reviews. 1 (1): MR000034. doi:10.1002/14651858.MR000034.pub3. ISSN 1469-493X. PMC 10765475. PMID 38174786.

Further reading