Multiple independent sample testing with proportions

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SUMMARY

This discussion focuses on analyzing multiple independent groups with proportion data in biological experiments. The user has encountered challenges with values of 0 and 1, leading to the exploration of arcsine transformations and Kolmogorov-Smirnov tests for significance. However, the user questions the appropriateness of these methods and their potential violation of statistical principles, similar to the misuse of repeated t-tests. The recommended approach includes using probit or logit models, pooling proportions across groups while employing dummy variables for group identification.

PREREQUISITES
  • Understanding of statistical tests, specifically Kolmogorov-Smirnov tests
  • Knowledge of transformation techniques, such as arcsine transformation
  • Familiarity with regression models, particularly probit and logit models
  • Basic concepts of dummy variables in statistical analysis
NEXT STEPS
  • Research the application of probit regression for proportion data analysis
  • Study the use of logit models in comparing independent groups
  • Learn about the implications of using arcsine transformations on data distribution
  • Explore best practices for handling binary data in statistical tests
USEFUL FOR

Biology students, researchers conducting experiments with proportion data, and statisticians looking to refine their analysis techniques for independent group comparisons.

jamesv87
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Hey everyone,

I'm working on my undergraduate thesis in biology and am having problems analyzing the data from my experiments. I'm wondering if anyone knows how to compare multiple independent groups when the measurements are proportions. The problem is that many of my values are 1 and 0 with relatively few in between. I've tried an arcsine transformation but it does not really help the distribution (Does it matter whether I use degrees or radians?). I've used Kolmogorov-Smirnov tests to compare between two groups at a time, and do find significance, which is reassuring, but I don't know if this is the best way to go about analyzing all of my data. Would I also be violating some rule of science if I did this, similar to using a repeated series of t-tests?

Your feedback is much appreciated!
 
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You can use probit or logit, with the left hand variable being the proportions pooled across groups, and the right hand variables a set of "dummy" variables that indicate each respective group.
 
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