In many occassions in research, we need to compare any two sets of measurements to quntify if there is any statistically significant difference or similarity.
There exist many Python/R packages that allow the scientists to perform detailed bayesian analysis. However, to even run a simple test dataset, it required a large amount of efforts in installation, understandign of programming language (Python or R), and experience in tweaking the model parameters and debugging in the whole process. To cut short all the time and efforts required for a non-coder, or even for myself, to repeatatively run similar statistical task in an efficient manner, I decided to develop a web-tool, using PyMC3 package. As a first release, it has already been a huge help for my colleagues in the computational biology research lab, promoting more the bayesian approach over t-test, as it is evident by our recent publications, where we incorporated this Bayesian approach in supporting our scientific observations/conclusions.
The statistical t-Test and p-value calculations are probably most popular methods in scientific practice to derive final conclusion. However, these traditional methods suffer many drawback, for example, two populations having exactly identical means, but different standard deviations, the traditional t-Test will fail to capture the significance in difference or similarity.
In short, full Bayesian Parameter Estimation method is a robust method in quantifying the significance in difference or similarity between any two populations under study.
Note, this tool is still under development, and I am adding more and more features to make it a varsatile and all-round tool.
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