The Complete Guide to Acing Your Statistics Interview Questions

Statistics interviews are becoming increasingly common as data science roles continue to grow in popularity. Whether you’re interviewing for a job as a data analyst, data scientist, or any other statistics-heavy role, you can expect to be asked challenging statistics questions to assess your skills and knowledge.

In this complete guide, we’ll cover everything you need to know to ace your statistics interview, from the most common basic questions to the more advanced queries.

Understanding the Basics

Let’s start with some fundamental statistics concepts that interviewers often quiz candidates on:

The Central Limit Theorem

The Central Limit Theorem states that the distribution of sample means approximates a normal distribution as the sample size gets larger, regardless of the actual distribution of the population being sampled This allows us to use methods based on the normal distribution even when the underlying population distribution is non-normal

Assumption of Normality

Many statistical analyses rely on the assumption that the data follows a normal distribution We check for normality to ensure the appropriateness of using certain statistical tests, Graphical methods like histograms and Q-Q plots along with numerical methods like skewness and kurtosis help assess normality

Hypothesis Testing

Hypothesis testing allows us to make statistical decisions using sample data. We start with a null hypothesis and an alternative hypothesis. Then we check if the sample data provides enough evidence to reject the null in favor of the alternative by checking the p-value or test statistic against some threshold.

Observational and Experimental Data

Observational data is collected by observing individuals or systems Experimental data is generated by setting up treatments, manipulating variables, and collecting data Experiments allow us to establish causality more strongly.

Outliers

Outliers are data points that lie an abnormal distance from other values. They can skew results. We identify them graphically or using statistical methods like Z-scores. Removing outliers requires investigation into why they occurred.

Intermediate Concepts

Let’s move on to some more intermediate statistics topics:

Screening for Outliers

We can screen for outliers by plotting data and looking for points far from the rest. Numeric methods include converting to Z-scores and removing points above an absolute value threshold like 3. Leverage and influence metrics also help identify particularly impactful outliers.

Inliers

Inliers are the opposite of outliers – they are values that lie within the normal range inside a cluster of data points. Density-based anomaly detection methods can help identify inliers and outliers.

Probability Distributions

Probability distributions describe the probabilities associated with different outcomes of a random variable. Important ones include the normal, binomial, Poisson and others. Knowing their formulas, shapes, key parameters, and when to use them is important.

Conditional Probability

Conditional probability refers to the probability of one event given that another event has occurred. Bayes’ theorem lets us calculate it using the inverse conditional probability and marginal probabilities.

Hypothesis Testing Errors

Type I error is rejecting the null hypothesis when it is true. Type II error involves not rejecting the null when the alternative hypothesis is true. Controlling significance level affects the types of errors made.

Confidence Intervals

Confidence intervals provide a range of plausible values for a population parameter based on a sample. Wider intervals indicate less precision but higher confidence in the inference. Interval width is affected by confidence level and sample size.

Advanced Topics

Finally, be ready for some advanced questions:

Statistical Power

Statistical power represents the probability of correctly rejecting the null hypothesis when it is false. It depends on significance level, sample size, and effect size. Larger samples give more power to detect effects.

Non-parametric Tests

Non-parametric tests make fewer assumptions, like not requiring normality. Examples include sign test, Wilcoxon signed rank, Spearman correlation. These are less powerful than parametric tests when assumptions hold.

Resampling Methods

Resampling methods like bootstrapping and permutation tests involve repeatedly drawing samples with replacement and recomputing test statistics. They allow hypothesis testing without distributional assumptions.

Bayesian vs Frequentist Statistics

Frequentist statistics relies on sampling distributions and significance testing. Bayesian statistics incorporates prior information and provides probabilities for hypotheses. Hybrid approaches leverage strengths of both paradigms.

Big Data Techniques

Big data techniques like dimensionality reduction, clustering, and sampling become important for dealing with massive datasets. MapReduce, Spark, and parallel processing enable efficient statistical analysis on big data.

With practice and preparation using questions on these key statistics concepts, you’ll be equipped to impress interviewers and land your ideal data role. Don’t be intimidated by the questions – being able to explain statistical ideas conversationally is an invaluable skill.