Comparing two sets of data: multiple time points

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SUMMARY

This discussion focuses on comparing two groups—control and treatment—across multiple time points to determine statistical significance for a single variable. The data is collected every five seconds over a 30-second period, leading to six time points. The recommended approach involves using probabilistic models, particularly those related to bacterial growth rates, to compute the likelihood of observed differences under the assumption that the growth parameter (lambda) is consistent across both groups. Specific statistical tests were not detailed, indicating a need for further clarification on the growth model used.

PREREQUISITES
  • Understanding of statistical significance testing
  • Familiarity with probabilistic models in biology
  • Knowledge of bacterial growth rate modeling
  • Experience with data collection and analysis over multiple time points
NEXT STEPS
  • Research statistical tests for comparing multiple time points, such as repeated measures ANOVA
  • Explore probabilistic models for bacterial growth, focusing on the lambda parameter
  • Learn about the assumptions required for statistical testing in biological experiments
  • Investigate software tools for statistical analysis, such as R or Python's SciPy library
USEFUL FOR

Researchers in microbiology, biostatisticians, and anyone involved in experimental design and analysis of treatment effects over time.

prime-factor
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I am trying to compare two groups (for statistical significance), a control and a treatment group across more than one time point, for a single variable. For example

Control Treatment
0 sec x x
5 sec x x
10 sec x x
15 sec x x
20 sec x x
30 sec x x

What sort of test can I use to compare these two groups for statistical significance, given the number of time points?
The data was collected every five seconds during a 30 second period.
 
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You haven't given enough information to get good advice. I'll assume you are doing something involving reaction rates or growth rates, since you've made some other posts about growing bacteria.

To do statistics, you must make enough assumptions to compute probabilities. I assume that people have invented probabilistic models for bacteria growth. Let's hope that they've invented one that has some single parameter lambda in it. Then one idea is to look at the probability of getting the observed differences in your two sets of data on the assumption that lambda is the same for both the treatment and control group. I assume you know more about models for bacterial growth rate that I do. If you can state one then perhaps I (or someone else) can make a more specific suggestion.
 

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