Estimation statistics

Estimation statistics, or simply estimation, is a data analysis framework that uses a combination of effect sizes, confidence intervals, precision planning, and meta-analysis to plan experiments, analyze data and interpret results.^[1] It complements hypothesis testing approaches such as null hypothesis significance testing (NHST), by going beyond the question is an effect present or not, and provides information about how large an effect is.^[2]^[3] Estimation statistics is sometimes referred to as the new statistics.^[3]^[4]^[5]

The primary aim of estimation methods is to report an effect size (a point estimate) along with its confidence interval, the latter of which is related to the precision of the estimate.^[6] The confidence interval summarizes a range of likely values of the underlying population effect. Proponents of estimation see reporting a P value as an unhelpful distraction from the important business of reporting an effect size with its confidence intervals,^[7] and believe that estimation should replace significance testing for data analysis.^[8]^[9]

^ Ellis, Paul. "Effect size FAQ".
^ Cohen, Jacob. "The earth is round (p<.05)" (PDF). Archived from the original (PDF) on 2017-10-11. Retrieved 2013-08-22.
^ ^a ^b Cite error: The named reference cumming was invoked but never defined (see the help page).
^ Altman, Douglas (1991). Practical Statistics For Medical Research. London: Chapman and Hall.
^ Douglas Altman, ed. (2000). Statistics with Confidence. London: Wiley-Blackwell.^{[page needed]}
^ Cite error: The named reference cohen was invoked but never defined (see the help page).
^ Ellis, Paul (2010-05-31). "Why can't I just judge my result by looking at the p value?". Retrieved 5 June 2013.
^ Claridge-Chang, Adam; Assam, Pryseley N (2016). "Estimation statistics should replace significance testing". Nature Methods. 13 (2): 108–109. doi:10.1038/nmeth.3729. PMID 26820542. S2CID 205424566.
^ Berner, Daniel; Amrhein, Valentin (2022). "Why and how we should join the shift from significance testing to estimation". Journal of Evolutionary Biology. 35 (6): 777–787. doi:10.1111/jeb.14009. ISSN 1010-061X. PMC 9322409. PMID 35582935. S2CID 247788899.

[1] Ellis, Paul. "Effect size FAQ".

[:0-2] Cohen, Jacob. "The earth is round (p<.05)" (PDF). Archived from the original (PDF) on 2017-10-11. Retrieved 2013-08-22.

[cumming-3] Cite error: The named reference cumming was invoked but never defined (see the help page).

[4] Altman, Douglas (1991). Practical Statistics For Medical Research. London: Chapman and Hall.

[:1-5] Douglas Altman, ed. (2000). Statistics with Confidence. London: Wiley-Blackwell.^{[page needed]}

[cohen-6] Cite error: The named reference cohen was invoked but never defined (see the help page).

[7] Ellis, Paul (2010-05-31). "Why can't I just judge my result by looking at the p value?". Retrieved 5 June 2013.

[8] Claridge-Chang, Adam; Assam, Pryseley N (2016). "Estimation statistics should replace significance testing". Nature Methods. 13 (2): 108–109. doi:10.1038/nmeth.3729. PMID 26820542. S2CID 205424566.

[9] Berner, Daniel; Amrhein, Valentin (2022). "Why and how we should join the shift from significance testing to estimation". Journal of Evolutionary Biology. 35 (6): 777–787. doi:10.1111/jeb.14009. ISSN 1010-061X. PMC 9322409. PMID 35582935. S2CID 247788899.

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