Replication (statistics)

In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions to support the original claim, which is crucial to confirm the accuracy of results as well as for identifying and correcting the flaws in the original experiment.[1] ASTM, in standard E1847, defines replication as "... the repetition of the set of all the treatment combinations to be compared in an experiment. Each of the repetitions is called a replicate."

For a full factorial design, replicates are multiple experimental runs with the same factor levels. You can replicate combinations of factor levels, groups of factor level combinations, or even entire designs. For instance, consider a scenario with three factors, each having two levels, and an experiment that tests every possible combination of these levels (a full factorial design). One complete replication of this design would comprise 8 runs (2^3). The design can be executed once or with several replicates.[2]

Example of direct replication and conceptual replication

There are two main types of replication in statistics. First, there is a type called “exact replication” (also called "direct replication"), which involves repeating the study as closely as possible to the original to see whether the original results can be precisely reproduced.[3] For instance, repeating a study on the effect of a specific diet on weight loss using the same diet plan and measurement methods. The second type of replication is called “conceptual replication.” This involves testing the same theory as the original study but with different conditions.[3] For example, Testing the same diet's effect on blood sugar levels instead of weight loss, using different measurement methods.

Both exact (direct) replications and conceptual replications are important. Direct replications help confirm the accuracy of the findings within the conditions that were initially tested. On the hand conceptual replications examine the validity of the theory behind those findings and explore different conditions under which those findings remain true. In essence conceptual replication provides insights, into how generalizable the findings are.[4]

  1. ^ Killeen, Peter R. (2008), "Replication Statistics", Best Practices in Quantitative Methods, 2455 Teller Road, Thousand Oaks California 91320 United States of America: SAGE Publications, Inc., pp. 102–124, doi:10.4135/9781412995627.d10, ISBN 978-1-4129-4065-8, retrieved 2023-12-11{{citation}}: CS1 maint: location (link)
  2. ^ "Replicates and repeats in designed experiments". support.minitab.com. Retrieved 2023-12-11.
  3. ^ a b "The Replication Crisis in Psychology". Noba. Retrieved 2023-12-11.
  4. ^ Hudson, Robert (2023-08-01). "Explicating Exact versus Conceptual Replication". Erkenntnis. 88 (6): 2493–2514. doi:10.1007/s10670-021-00464-z. ISSN 1572-8420. PMC 10300171. PMID 37388139.