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Null result

In science, a null result is a result without the expected content: that is, the proposed result is absent.[1] It is an experimental outcome which does not show an otherwise expected effect. This does not imply a result of zero or nothing, simply a result that does not support the hypothesis.

In statistical hypothesis testing, a null result occurs when an experimental result is not significantly different from what is to be expected under the null hypothesis; its probability (under the null hypothesis) does not exceed the significance level, i.e., the threshold set prior to testing for rejection of the null hypothesis. The significance level varies, but common choices include 0.10, 0.05, and 0.01.[2] However, a non-significant result does not necessarily mean that an effect is absent.[3][4][5][6]

As an example in physics, the results of the Michelson–Morley experiment were of this type, as it did not detect the expected velocity relative to the postulated luminiferous aether. This experiment's famous failed detection, commonly referred to as the null result, contributed to the development of special relativity. The experiment did appear to measure a non-zero "drift", but the value was far too small to account for the theoretically expected results; it is generally thought to be inside the noise level of the experiment.[7]

  1. ^ Giunti, C.; et al. (1999). "New ordering principle for the classical statistical analysis of Poisson processes with background". Phys. Rev. D. 59 (5): 053001. arXiv:hep-ph/9808240. Bibcode:1999PhRvD..59e3001G. doi:10.1103/PhysRevD.59.053001. S2CID 14948954.
  2. ^ Casella, George; Berger, Roger (2002). Statistical Inference (2nd ed.). Duxbury. p. 385. ISBN 0-534-24312-6.
  3. ^ Lakens, Daniël (2017). "Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses". Social Psychological and Personality Science. 8 (4): 355–362. doi:10.1177/1948550617697177. ISSN 1948-5506. PMC 5502906. PMID 28736600.
  4. ^ Gross, Justin H. (2015). "Testing What Matters (If You Must Test at All): A Context-Driven Approach to Substantive and Statistical Significance". American Journal of Political Science. 59 (3): 775–788. doi:10.1111/ajps.12149. ISSN 0092-5853.
  5. ^ Hartman, Erin; Hidalgo, F. Daniel (2018). "An Equivalence Approach to Balance and Placebo Tests". American Journal of Political Science. 62 (4): 1000–1013. doi:10.1111/ajps.12387. hdl:1721.1/126115. ISSN 0092-5853.
  6. ^ Rainey, Carlisle (2014). "Arguing for a Negligible Effect". American Journal of Political Science. 58 (4): 1083–1091. doi:10.1111/ajps.12102. ISSN 0092-5853.
  7. ^ "Role of the Michelson-Morley experiments in making determinations about competing theories". Archived from the original on 2012-11-07. Retrieved 2003-07-17.

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