Interview Questions

Sam A. - "The distribution of daily search queries per user, as shown in the histogram, can be described as approximately normal (or bell-shaped) with a slight positive skew. Key Characteristics: Shape: The distribution is roughly symmetrical around its center, resembling a bell curve. This indicates that most users perform a moderate number of daily search queries. Central Tendency: The peak of the distribution, representing the highest density of users, appears to be around **8"See full answer

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

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

Anawar B. - "Use this section submitting solution and feedback to athers"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

Gopala krishna A. - "I think there’s a small issue in the regression suggestion. Regressing the KPI on bucket dummies and flagging significance doesn’t necessarily indicate flawed randomization, it could just mean the treatment had an effect, which is what we’re testing. For a randomization check, we usually compare pre-treatment baseline covariates (age, geography, device, prior activity, etc.) across buckets via descriptive stats / t-tests / chi-square, or regress each baseline covariate on bucket indicators (or r"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

Sinchita S. - "Statistical power is defined as the probability that a test will correctly reject a false null hypothesis. In other words, it is the likelihood of detecting an effect (e.g. a real difference between two groups) if one actually exists. It is typically set to 80% meaning that 80% of the time we will can correctly detect a difference between the groups. It is also a critical component of calculating the correct sample size for an experiment. Let's say if we conduct an experiment on a very small sam"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

Hustle - "What does Netflix do? Netflix is a global entertainment company that produces and streams movies, TV shows, and documentaries. In recent years, it has also expanded into games and short-form content to increase engagement and strengthen its ecosystem. Why introduce podcasts? Netflix’s mission is to entertain the world. Podcasts offer a new, low-effort way for users to engage with Netflix’s stories and creators, especially during non-screen moments like commuting, exercising, or cooking. In"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