30 interview questions with answers on Descriptive and Inferential Statistics

 Here are 30 interview questions with answers on Descriptive and Inferential Statistics, organized into relevant categories to help students, researchers, and job seekers in analytics, finance, research, or data science fields:

🔹 A. Basics & Definitions (1–10)

  1. What is descriptive statistics?
    Descriptive statistics summarize and organize features of a data set using measures such as mean, median, mode, standard deviation, and graphs.

  2. What is inferential statistics?
    Inferential statistics involve drawing conclusions about a population based on a sample through hypothesis testing, confidence intervals, and regression analysis.

  3. What are the major types of descriptive statistics?

    • Measures of central tendency: Mean, Median, Mode

    • Measures of dispersion: Range, Variance, Standard Deviation

    • Shape: Skewness, Kurtosis

  4. What are the main tools used in inferential statistics?

    • Hypothesis testing

    • Confidence intervals

    • t-tests, z-tests

    • ANOVA

    • Chi-square test

    • Regression analysis

  5. What is the difference between population and sample?

    • Population: Entire group of interest

    • Sample: Subset drawn from the population used for analysis

  6. What is the role of probability in inferential statistics?
    Probability helps assess the likelihood that sample results reflect the population and are not due to chance.

  7. What is a parameter and what is a statistic?

    • Parameter: A numeric summary of a population

    • Statistic: A numeric summary of a sample

  8. What is the difference between qualitative and quantitative data?

    • Qualitative: Non-numeric, categorical (e.g., gender, color)

    • Quantitative: Numeric, measurable (e.g., income, weight)

  9. What is the importance of descriptive statistics in research?
    It helps in summarizing large datasets, making the data more understandable and easier to visualize.

  10. Why do we use inferential statistics in research?
    To make generalizations, predictions, or test hypotheses about a population based on sample data.

🔹 B. Descriptive Statistics Techniques (11–17)

  1. What is the mean, and how is it calculated?
    The mean is the average: sum of values divided by number of values.

  2. What is the median?
    The middle value in an ordered data set. If even number of values, it’s the average of the two middle ones.

  3. What is the mode?
    The most frequent value in a data set.

  4. What is range?
    Difference between the highest and lowest values.

  5. What is standard deviation?
    A measure of how spread out the values are around the mean.

  6. What is variance?
    The average of the squared deviations from the mean. It’s the square of the standard deviation.

  7. What is skewness and what does it indicate?
    Skewness measures asymmetry in a distribution.

  • Positive skew: Tail on the right

  • Negative skew: Tail on the left

🔹 C. Inferential Statistics Techniques (18–27)

  1. What is hypothesis testing?
    A method to test assumptions about a population using sample data.

  2. What is a null hypothesis (H₀)?
    A statement that there is no effect or no difference; it is what we test against.

  3. What is an alternative hypothesis (H₁)?
    A statement that there is an effect or difference.

  4. What is a p-value?
    Probability of observing the data (or more extreme) if the null hypothesis is true.

  • If p < 0.05, reject H₀.

  1. What is a confidence interval?
    A range of values likely to contain the population parameter with a certain level of confidence (usually 95%).

  2. What is the difference between a t-test and a z-test?

  • Z-test: Used when population variance is known and sample size is large

  • T-test: Used when population variance is unknown and sample size is small

  1. What is ANOVA?
    Analysis of Variance: Compares the means of 3 or more groups to test if at least one differs.

  2. What is regression analysis?
    A technique used to examine the relationship between a dependent variable and one or more independent variables.

  3. What is a Type I error?
    Rejecting a true null hypothesis (false positive).

  4. What is a Type II error?
    Failing to reject a false null hypothesis (false negative).

🔹 D. Applications & Interpretation (28–30)

  1. How do you interpret a 95% confidence interval?
    We are 95% confident that the true population parameter lies within the interval.

  2. Why is descriptive statistics not enough in research?
    It does not allow generalization beyond the sample; inferential statistics is needed for decision-making.

  3. Can you give an example where both descriptive and inferential statistics are used?
    In a study analyzing test scores of students:

  • Descriptive: Average score, standard deviation

  • Inferential: Whether students from different classes scored significantly differently (ANOVA)


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