Calculating Sample Size for an Olympic Sprinter Experiment

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Discussion Overview

The discussion revolves around calculating the required sample size for an experiment aimed at assessing the effect of a new energy drink on Olympic sprinters' performance in a 100m race. The conversation includes considerations of statistical power, Type I error, and hypotheses related to performance differences.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Pistone outlines the requirements for the experiment, including a Type I error rate of less than 0.05 and a power greater than 0.80, seeking help to determine the sample size for various performance difference thresholds.
  • Some participants suggest that the problem can be viewed as the inverse of determining alpha and beta given sample sizes and means.
  • Pistone expresses a lack of statistical expertise and requests further assistance in understanding the calculations needed.
  • Additional information is provided by Pistone regarding the null and alternate hypotheses, specifying that the alternate hypothesis involves various performance improvements.
  • A participant shares a link to a Wikipedia page that explains sample size calculations for hypothesis tests.
  • Another participant discusses their own work with molecular data and inquires about the impact of sample size on statistical tests, specifically mentioning the Kruskal-Wallis test.
  • One participant confirms that the Kruskal-Wallis test is a suitable approximation but notes its assumption of identically-shaped distributions across groups.
  • The same participant asks how sample size influences the choice of statistical analysis tools and requests references on the topic.

Areas of Agreement / Disagreement

Participants generally agree on the need for statistical calculations related to sample size, but there is no consensus on the specific methods or tools to use. Additionally, the discussion includes multiple perspectives on statistical tests suitable for different types of data.

Contextual Notes

Participants express varying levels of statistical knowledge, which may affect the depth of the discussion. There are also references to specific tools and methods that may not be universally applicable or understood by all participants.

Who May Find This Useful

This discussion may be useful for individuals interested in experimental design, particularly in sports science, as well as those seeking to understand the relationship between sample size and statistical analysis methods.

Pistone
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Dear forum pal,

Here is my problem:

- We would like to buid an experiment in order to check the effect of a new energy drink that is destined to the olympic sprinters (100m).

- Here are our requirements for the experiment.

- Type I error = alpha = less than 0.05
- Power = 1-beta = better than 0.80

If the results difference over 100 meter between someone who used the drink and someone who did not is 0.1%, what is the required sampling size ?

Same question for a difference of 0.2 %, 2.5%, 3.6%, 4.0% and 4.7%


Please try to help us.
If our question lacks any other information please let me know.

Pistone.
 
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Think of this as the mirror image of the "usual" problem "given sample size, the two means and the two standard dev.s (under the null and the alternative), determine the alpha and the beta."
 
Thank you EnumaElish

Dear EnumaElish,

Thank you very much for taking the time to answer me.

Unfortunately for me, I am very weak in statistics as my basic expertise is Food Formulation and Innovation.

Please can you help me more ?

Thank you again for your kind help.

Best Regards,
Pistone.
 
Additional Information

Dear Forum Pals,

It looks like my request lacks some basic information.

Please receive the missing information:

- The null hypothesis is that there is no difference between a sprinter that have consumed the drink and a sprinter that have consumed mineral water.

-The Alternate hypothesis is that there is a difference between the 2 sprinters and that this difference improves the results in seconds (over 100m) of 0.1% (sprinter with energy drink better than sprinter with water). Other experiments would have alternate hypothesis of improving the results for 0.2%, 2.5%, 3.6%, 4.0% and 4.7%.

-Concerning the beta, since beta=(1-statistical power of the experiment) and since such experiments in order to be valid should have a power of at least 0.80 then beta=0.2

I have found a nice site that makes automatic calculations I don't know how to use it properly (I am weak in statistics, my basic expertise is Food Formulation and Innovation). I wonder if we can post links on this forum but you can find the tool on dssresearch.com then click researcher's toolkit and then sample size calculator and then percentages. But you are the experts, You surely know better than me how to find the answer...

Thank you again for your precious help, it is highly appreciated.

Best Regards,
Pistone
 
Hello all,
I am working with molecular data and my aim is determinate if there are differences in the quantity of 17 molecular markers between 4 populations (N=72 [rows] x 17 markers [columns] each one found in different proportion in the samples, then I calculate the mean by population by each marker and try to make statistics). How can I choose a good statistical test with these sample size? How much affect the sample size the statistical test? The data not follow a normal distribution and I use Kruskal-Wallis test to make the comparison, but I am not sure if this test suits with this kind of data. Please, help!. Thank you :)
 
Thanks EnumaElish, I taken into account this issue.
I have another question... How the sample size affect the selection of the statistical analysis tools? Any reference for that?
Again thank you! :)
 

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