Statistics & Experimentation Interview Questions

Vineet M. - "The distribution of daily minutes spent on Facebook per user is heavily right-skewed with a long tail. Most users spend a short amount of time while a smaller segment of heavy users push up the average with 2–3+ hours daily."See full answer

Saurabh K. - "P(A) = 0.6 P(B) = 0.4 P(D|A) = 0.05 P(D|B) = 0.03 Question asks to solve for P(A|D) P(A|D) = (P(D|A) x P(A))/P(D) = (0.05 x 0.6)/(P(D|A) x P(A) + P(D|B) x P(B)) = (0.05 x 0.6)/(0.05 x 0.6+0.03 x 0.4) = 30/42 = 5/7 = 0.714 Notice above that P(D) = P(D|A) x P(A) + P(D|B) x P (B)"See full answer

Sangeeta P. - "A/B testing is used when one wishes to only test minor front-end changes on the website. Consider a scenario where an organization wishes to make significant changes to its existing page, such as wants to create an entirely new version of an existing web page URL and wants to analyze which one performs better. Obviously, the organization will not be willing to touch the existing web page design for comparison purposes. In the above scenario, performing Split URL testing would be beneficial. T"See full answer

Sangeeta P. - "Marketing campaigns are run through different channels such as social media, emails, SEO, web advertising, events, etc. Let’s look at some of the overall success metrics at a broader level: Total views for your campaign Unique views for your campaign Returning visitors for your campaign Engagement for your campaign (If it’s a social media campaign, the marketer might be interested in knowing the number of users engaging with the campaign and the type of campaign positive/negative) 5"See full answer

Lucas G. - "To speed up A/B tests results with limited sample sizes, we can apply advanced techniques like CUPED to reduce variance for faster statistical significance, interleaving to gather more comparative data per user (e.g., ranking), MAB to dynamically allocate traffic to winning variations for quicker optimization (e.g., campaigns), and Bayesian A/B testing which offers probabilistic conclusions that can be reached earlier. Each method, when appropriately applied, allows you to gain m"See full answer

Lucas G. - "Because testing many engagement metrics at once increases the risk of finding effects that aren't real (the 'multiple comparisons problem'), you must adjust your criteria for statistical significance. For social media data, the Benjamini-Hochberg procedure is often a practical choice as it controls the rate of false discoveries (FDR) while still allowing you to detect genuine changes; however, the ideal adjustment method will vary depending on your specific number of metrics (e.g., use Bonferron"See full answer

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

Connor W. - "This video is a duplicate of the other video in this lesson, "Design A/B test for New Campaign""See full answer

Saurabh K. - "Ask a follow up question : What is the primary goal of expanding into a new vertical ? Food vertical company may want to expand to a new vertical (say Grocery) for the following reasons : Attract new customers interested in grocery delivery instead of food delivery Increase usage/order frequency from existing customers Increase revenue and LTV of existing as well as potentially new customers Benefit from synergies between existing delivery engine by improving utilization of their network"See full answer

Himani E. - "The cases where data is under heavy outlier influence. Since mean fluctuates due to the presence of an outlier, median might be a better measure"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