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Introduction to Hypothesis Tests and Confidence Intervals Questions

Hypothesis testing and confidence intervals are essential tools in data science for:

  • Making inferences from samples
  • Guiding data-driven decision-making
  • Validating assumptions
  • Quantifying uncertainty
  • Enabling comparisons
  • Effectively communicating findings to stakeholders.

These tools form the basis of A/B testing, which is widely used by tech companies to test new features before launching them.

This module will cover how to prepare for and answer questions about hypothesis testing and confidence intervals. The following lessons will teach how to answer hypothesis test questions and calculate confidence intervals.

Interview questions related to these topics involve a mix of:

  1. Conceptual questions (e.g. “What is a p-value?”).
  2. Applied questions (e.g. “Given results from a hypothesis test, what can you infer?”). The interview may also present a case study and ask you to design a hypothesis test.

How to prepare

  • Understand the concepts in this lesson. Know the definitions, assumptions, and procedures for different types of hypothesis tests (e.g., t-tests, chi-square tests), how to design and implement a hypothesis test, and how to estimate confidence intervals.
  • Review mathematical formulas. Understand how to compute test statistics, p-values, and confidence limits for different types of tests and intervals. Work through 1-2 example problems per topic in this lesson.
  • Use statistical software. Practice performing analyses and interpreting results using statistical software packages commonly used for hypothesis testing and confidence interval calculations, such as R and Python (with libraries like SciPy and statsmodels).
  • Interpret real-world hypothesis tests and confidence intervals. Understand what different outcomes (e.g. rejecting the null hypothesis, failing to reject the null hypothesis) imply for the research question or problem being addressed. Consider real-world scenarios where these techniques might be used, the implications of different outcomes, and what your recommendations would be.
  • Practice communicating these concepts. Be able to explain concepts, procedures, and interpretations to someone who may not be familiar with statistics. Data scientists often explain these concepts at a high level to non-technical stakeholders when sharing their analyses.
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