There are two possible outcomes: if the result confirms the hypothesis, then you've made a measurement. If the result is contrary to the hypothesis, then you've made a discovery - Enrico Fermi
You may use any of the following libraries:
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Pandas
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SciPy
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Statsmodels
You must perform an AB test in order to decide whether the introduction of tailored ads will improve the current Click-Through Rate on your app. The notebook included in this repo will guide you throughout the process. However, you must have a clear understanding of:
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The metrics involved in the test (EDA).
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The experiment design.
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The behaviour before the experiment.
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The behaviour after the experiment.
IMPORTANT: Hypothesis testing can be a valuable tool when used appropriately, but it is important to remember that it is not infallible. It is subject to error, and the results are influenced by factors such as the sample size, the level of significance chosen, and the assumptions underlying the statistical model. Therefore, it is important to interpret the results of hypothesis testing with caution and to consider the context of the research question and the limitations of the data.
