2.1.6 Sample size and power analysis - Revision history

Antonbespalov: /* C. Resources */

2021-09-14T07:43:29Z

‎C. Resources

ChristophEmmerich at 12:46, 24 March 2021

2021-03-24T12:46:10Z

85.216.81.116: /* C. Resources */

2020-11-04T14:26:31Z

‎C. Resources

85.216.81.116: /* C. Resources */

2020-10-29T08:40:42Z

‎C. Resources

85.216.81.116: /* B. Guidance & Expectations */

2020-10-22T06:51:17Z

‎B. Guidance & Expectations

85.216.81.116: /* B. Guidance & Expectations */

2020-10-22T06:48:20Z

‎B. Guidance & Expectations

Bjoerngerlach at 06:46, 21 October 2020

2020-10-21T06:46:01Z

Bjoerngerlach at 06:41, 21 October 2020

2020-10-21T06:41:40Z

85.216.81.116: /* B. Guidance & Expectations */

2020-10-21T06:24:36Z

‎B. Guidance & Expectations

85.216.81.116: /* B. Guidance & Expectations */

2020-10-19T15:45:53Z

‎B. Guidance & Expectations

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 * [https://wise1.cgu.edu/power/index.asp WISE power tutorial]
 * [http://davidmlane.com/hyperstat/power.html JAVA applets for power and sample size]
-* [https://www.psychometrica.de/effect_size.html Computation of sample sizes @Psychometrica]
+* [https://www.psychometrica.de/effect_size.html Computation of effect sizes @Psychometrica]
 * [http://powerandsamplesize.com/Calculators/ Overview of sample size and power calculators]

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 * make sure that the most suited randomization schedule is used to control for random influences
 * explore and engage all other means to minimize variation (including using properly maintained and calibrated research instruments, adequate and well controlled environmental conditions, making sure that experiments are performed by competent and adequately trained scientists)
-* if a study has low power because of the given sample size, reflect this limitation in the study plan and indicate to all stakeholders that the study cannot be run as knowledge-claiming (decision-enabling, confirmatory).
+* if a study has low power because of the given sample size, reflect this limitation in the study protocol and indicate to all stakeholders that the study cannot be run as knowledge-claiming (decision-enabling, confirmatory).
 * evaluate power not only for the given sample size for also for the values around and discuss the impact of the sample size on power with the stakeholders - in some cases, it may help to lift or revise the original sample size restrictions. These discussions make sense and are justifiable only if they take place prior to the conduct of the study (i.e. not post hoc).

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 Useful literature:
-[https://pdfs.semanticscholar.org/1325/24bdfe70504fcd67016b17305ccddb4bcd14.pdf Power in various ANOVA designs by Joel Levin]
+* [https://pdfs.semanticscholar.org/1325/24bdfe70504fcd67016b17305ccddb4bcd14.pdf Power in various ANOVA designs by Joel Levin]
 * [https://www.ncbi.nlm.nih.gov/books/NBK43321/ https://www.ncbi.nlm.nih.gov/books/NBK43321/]

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 * [https://www.ncbi.nlm.nih.gov/books/NBK43321/ https://www.ncbi.nlm.nih.gov/books/NBK43321/]
 * [http://davidmlane.com/hyperstat/power.html http://davidmlane.com/hyperstat/power.html]
-* [http://powerandsamplesize.com/Calculators/Test-1-Mean/1-Sample-Equality http://powerandsamplesize.com/Calculators/Test-1-Mean/1-Sample-Equality]
+* [http://powerandsamplesize.com/Calculators/Test-1-Mean/1-Sample-Equality http://powerandsamplesize.com/Calculators]
 Guidelines on reporting of sample size (in vivo research):

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 * explore and engage all other means to minimize variation (including using properly maintained and calibrated research instruments, adequate and well controlled environmental conditions, making sure that experiments are performed by competent and adequately trained scientists)
 * if a study has low power because of the given sample size, reflect this limitation in the study plan and indicate to all stakeholders that the study cannot be run as knowledge-claiming (decision-enabling, confirmatory).
-* evaluate power not only for the given sample size for also for the values around and discuss the impact of the sample size on power with the stakeholders - in some cases, it may help reconsider the original sample size restrictions.
+* evaluate power not only for the given sample size for also for the values around and discuss the impact of the sample size on power with the stakeholders - in some cases, it may help to lift or revise the original sample size restrictions. These discussions make sense and are justifiable only if they take place prior to the conduct of the study (i.e. not post hoc).
 == C. Resources ==

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 * Put science before statistics. It is easy to get caught up in statistical significance and such; but studies should be designed to meet scientific goals, and you need to keep those in sight at all times (in planning and analysis). The appropriate inputs to power/sample-size calculations are effect sizes that are deemed scientifically important, based on careful considerations of the underlying scientific (not statistical) goals of the study. Statistical considerations are used to identify a plan that is effective in meeting scientific goals – not the other way around.
 * Do pilot studies. Investigators tend to try to answer all the world’s questions with one study. However, you usually cannot do a definitive study in one step. It is far better to work incrementally. A pilot study helps you establish procedures, understand and protect against things that can go wrong, and obtain variance estimates needed in determining sample size. A pilot study with 20-30 degrees of freedom for error is generally adequate for obtaining reasonably reliable sample-size estimates.
 * Generate sample size estimates for a range of power and effect size values to explore the gains and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).
 * Remember to consider attrition rate (i.e. possibility that some subjects or samples are lost during the conduct of the study or follow-up for technical and other data analysis-unrelated reasons)

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 == A. Background & Definitions ==
-Statistical power is defined as the probability of detecting as statistically significant an effect of a pre-specified size. Formally, power is equal to 1 minus the Type II error rate (beta or ß). The Type II error rate is the probability of obtaining a non-significant result when the null hypothesis is false — in other words, failing to find a difference or relationship when one exists.
+Statistical power is defined as the probability of detecting a statistically significant effect of a pre-specified size. Formally, power is equal to 1 minus the Type II error rate (beta or ß). The Type II error rate is the probability of obtaining a non-significant result when the null hypothesis is false — in other words, failing to find a difference or relationship when one exists.
 Balancing sample size, effect size and power is critical to good study design. When the power is low, only large effects can be detected, and negative results cannot be reliably interpreted. The consequences of low power are particularly dire in the search for high-impact results, when the researcher may be willing to pursue low-likelihood hypotheses for a groundbreaking discovery (see Fig. 1 in [https://www.nature.com/articles/nmeth.2738 Krzywinski & Altman 2013]). Ensuring that sample sizes are large enough to detect the effects of interest is an essential part of study design.
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 * Do pilot studies. Investigators tend to try to answer all the world’s questions with one study. However, you usually cannot do a definitive study in one step. It is far better to work incrementally. A pilot study helps you establish procedures, understand and protect against things that can go wrong, and obtain variance estimates needed in determining sample size. A pilot study with 20-30 degrees of freedom for error is generally adequate for obtaining reasonably reliable sample-size estimates.
 * Effect size should be specified on the actual scale of measurement, not on a standardized scale.
-* Generate sample size estimates for a range of power and effect size values to explore the gains and and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).
+* Generate sample size estimates for a range of power and effect size values to explore the gains and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).
 * Remember to consider attrition rate (i.e. possibility that some subjects or samples are lost during the conduct of the study or follow-up for technical and other data analysis-unrelated reasons)

← Older revision		Revision as of 06:24, 21 October 2020
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	* Effect size should be specified on the actual scale of measurement, not on a standardized scale.		* Effect size should be specified on the actual scale of measurement, not on a standardized scale.
	* Generate sample size estimates for a range of power and effect size values to explore the gains and and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).		* Generate sample size estimates for a range of power and effect size values to explore the gains and and losses in power or detectable effect size due to increasing or decreasing n. This is why the term ‘sample size estimation’ is often preferred over ‘sample size calculation’. Although the arrival at a number for the required sample size is invariably based on (often complex) formulae, the term ‘calculation’ implies an unwarranted degree of precision. The purpose of sample size estimation is not to give an exact number but rather to subject the study design to scrutiny, including an assessment of the validity and reliability of data collection ([https://www.sciencedirect.com/science/article/pii/S1466853X05000714 Batterham & Atkinson 2005]).
		+	* Remember to consider attrition rate (i.e. possibility that some subjects or samples are lost during the conduct of the study or follow-up for technical and other data analysis-unrelated reasons)

← Older revision		Revision as of 15:45, 19 October 2020
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	Guidance on sample size estimation:		Guidance on sample size estimation:

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		+	.. getting a solid grip on the existing literature in one's topic, drilling down to what effects were identified and obtaining the corresponding ES values either directly from the publication or from appropriate calculations based on the printed documentation.
		+
		+	.. being sure that the estimate you obtain is the one that fits the study design correctly; one cannot necessarily generalize across disparate research designs.
		+
		+	.. and citing the algorithm or software used to generate the estimates. A power calculation result given without this detail can be viewed with suspicion.
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		+	--- to be added / revised (please do not edit - placeholder) ---

2.1.6 Sample size and power analysis - Revision history

Antonbespalov: /* C. Resources */

ChristophEmmerich at 12:46, 24 March 2021

85.216.81.116: /* C. Resources */

85.216.81.116: /* C. Resources */

85.216.81.116: /* ​B. Guidance & Expectations */

85.216.81.116: /* ​B. Guidance & Expectations */

Bjoerngerlach at 06:46, 21 October 2020

Bjoerngerlach at 06:41, 21 October 2020

85.216.81.116: /* ​B. Guidance & Expectations */

85.216.81.116: /* ​B. Guidance & Expectations */

85.216.81.116: /* B. Guidance & Expectations */

85.216.81.116: /* B. Guidance & Expectations */

85.216.81.116: /* B. Guidance & Expectations */

85.216.81.116: /* B. Guidance & Expectations */