Learn

Congratulations! You now know how to run a binomial hypothesis test using a SciPy function — or by simulating it yourself! This will serve you well as a data scientist because it will enable you to investigate what’s going on if pre-written functions return surprising results. You also now have a conceptual understanding of how a binomial test works and what questions it aims to answer. To summarize, here are some of the things you’ve learned about hypothesis tests in general:

  • All hypothesis tests start with a null and alternative hypothesis

  • Outcomes of a hypothesis test that might be reported include:

    • confidence intervals
    • p-values
  • A hypothesis test can be simulated by:

    • taking repeated random samples where the null hypothesis is assumed to be true
    • using those simulated samples to generate a null distribution
    • comparing an observed sample statistic to that null distribution

Instructions

As a final exercise, the solution code for the previous exercise is available to you in script.py. As a challenge, see if you can re-write the simulation-based binomial test function so that it has an input named alternative_hypothesis that can be equal to 'less', 'not_equal', or 'greater'. Then change the function so that it performs the appropriate one- or two-sided test for the alternative hypothesis provided. Solution code is available to you in solution.py if interested.

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