analytical statistician Interview Questions and Answers

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100 Interview Questions and Answers for Analytical Statistician

  1. What is the difference between descriptive and inferential statistics?

    • Answer: Descriptive statistics summarizes and describes the main features of a dataset, using measures like mean, median, mode, and standard deviation. Inferential statistics uses sample data to make inferences about a larger population, employing techniques like hypothesis testing and confidence intervals.

  2. Explain the central limit theorem.

    • Answer: The central limit theorem states that the distribution of the sample means approaches a normal distribution as the sample size gets larger, regardless of the shape of the population distribution. This is crucial for statistical inference as it allows us to use normal distribution-based methods even when the population distribution is unknown.

  3. What are the assumptions of linear regression?

    • Answer: Linear regression assumes linearity, independence of errors, homoscedasticity (constant variance of errors), normality of errors, and no multicollinearity (low correlation between predictor variables).

  4. How do you handle missing data?

    • Answer: Methods for handling missing data include deletion (listwise or pairwise), imputation (mean, median, mode imputation, regression imputation, k-nearest neighbors), and maximum likelihood estimation. The best approach depends on the type of missing data (MCAR, MAR, MNAR) and the amount of missing data.

  5. Explain the difference between Type I and Type II errors.

    • Answer: A Type I error (false positive) occurs when we reject a true null hypothesis. A Type II error (false negative) occurs when we fail to reject a false null hypothesis. The significance level (alpha) controls the probability of a Type I error, while the power of a test (1-beta) controls the probability of avoiding a Type II error.

  6. What is p-value?

    • Answer: The p-value is the probability of observing results as extreme as, or more extreme than, the results actually obtained, assuming the null hypothesis is true. A small p-value (typically below a significance level like 0.05) provides evidence against the null hypothesis.

  7. What is a confidence interval?

    • Answer: A confidence interval is a range of values that is likely to contain the true population parameter with a certain level of confidence (e.g., 95%). It provides a measure of uncertainty around the point estimate.

  8. Explain the difference between correlation and causation.

    • Answer: Correlation measures the association between two variables, while causation implies that one variable directly influences the other. Correlation does not imply causation; two variables can be correlated without one causing the other (e.g., ice cream sales and drowning incidents are correlated, but one doesn't cause the other).

  9. What are some common statistical software packages you're familiar with?

    • Answer: R, Python (with libraries like pandas, NumPy, scikit-learn), SAS, SPSS, Stata.

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