Interview Questions

Lucas G. - "I would conduct a sample z-test because we have enough samples and the population variance is known. H1: average monthly spending per user is $50 H0: average monthly spending per user is greater $50 One-sample z-test x_bar = $85 mu = $50 s = $20 n = 100 x_bar - mu / (s / sqrt(n) = 17.5 17.5 is the z-score that we will need to associate with its corresponding p-value. However, the z-score is very high, so the p-value will be very close to zero, which is much less than the standa"See full answer

Lucas G. - "Null hypothesis (H0): the coin is fair (unbiased), meaning the probability of flipping a head is 0.5 Alternative (H1): the coin is unfair (biased), meaning the probability of flipping a head is not 0.5 To test this hypothesis, I would calculate a p-value which is the probability of observing a result as extreme as, or more extreme than, what I say in my sample, assuming the null hypothesis is true. I could use the probability mass function of a binomial random variable to model the coin toss b"See full answer

Lucas G. - "The central limit theorem tells us that as we repeat the sampling process of an statistic (n > 30), the sampling distribution of that statistic approximates the normal distribution regardless of the original population's distribution. This theorem is useful because it allows us to apply inference with tools that assume normality like t-test, ANOVA, calculate p-values hypothesis testing or regression analysis, calculate confidence intervals, etc."See full answer

Lucas G. - "A confidence interval gives you a range of values where you can be reasonably sure the true value of something lies. It helps us understand the uncertainty around an estimate we've measured from a sample of data. Typically, confidence intervals are set at the 95% confidence level. For example, A/B test results show that variant B has a CTR of 10.5% and its confidence intervals are [9.8%, 11.2%], this means that based on our sampled data, we are 95% confident that the true avg CTR for variant B a"See full answer

Lucas G. - "I'd recommend to adjust p-values because of the increased chance of type I errors when conducting a large number of hypothesis. My recommended adjustment approach would be the Benjamini-Hochberg (BH) over the Bonferroni because BH strikes a balance between controlling for false positive and maintaining statistical power whereas Bonferroni is overly conservative while still controlling for false positives, it leads to a higher chance of missing true effects (high type II error)."See full answer

Lucas G. - "Type I error (typically denoted by alpha) is the probability of mistakenly rejecting a true null hypothesis (i.e., We conclude that something significant is happening when there's nothing going on). Type II (typically denoted by beta) error is the probability of failing to reject a false null hypothesis (i.e., we conclude that there's nothing going on when there is something significant happening). The difference is that type I error is a false positive and type II error is a false negative. T"See full answer

Emiliano I. - "Look for the main variables and see if there differences in the distributions of the buckets. Run a linear regression where the dependent variable is a binary variable for each bucket excluding one and the dependent variable is the main kpi you want to measure, if one of those coefficients is significant, you made a mistake. "See full answer

Mark S. - "E(VAR(X))= VAR(X) VAR(X)= E[(X-E(X))^2] = E[X^2]-E[X]^2"See full answer

Mark S. - "Range captures the difference between the highest and lowest value in a data set, while standard deviation measures the variation of elements from the mean. Range is extremely sensitive to outliers, it tells us almost nothing about the distribution of the data, and does not extrapolate to new data (a new value outside the range would invalidate the calculation). Standard deviation, on the other hand, offers us an insight into how closely data is distributed towards the mean, and gives us some pr"See full answer

Dhruv M. - "Thankyou for asking me this answer. What makes me unique in data analytics is my ability to blend technical skills with a strong business mindset. I don’t just focus on building dashboards or running analyses-I always tie the insights back to real business impact. During my internship at Quantara Analytics, for example, I didn’t just track supplier KPI's. I redesigned the reporting process, which cut manual work by 60% and improved decision-making. I’m also proactive about learning tools like Po"See full answer