KDnuggets Home » News » 2016 » Feb » Tutorials, Overviews » 21 Must-Know Data Science Interview Questions and Answers ( 16:n06 )

2016 Gold Blog21 Must-Know Data Science Interview Questions and Answers

KDnuggets Editors bring you the answers to 20 Questions to Detect Fake Data Scientists, including what is regularization, Data Scientists we admire, model validation, and more.

8. What is statistical power?

Answer by Gregory Piatetsky:

Wikipedia defines Statistical power or sensitivity of a binary hypothesis test is the probability that the test correctly rejects the null hypothesis (H0) when the alternative hypothesis (H1) is true.

To put in another way, Statistical power is the likelihood that a study will detect an effect when the effect is present. The higher the statistical power, the less likely you are to make a Type II error (concluding there is no effect when, in fact, there is).

Here are some tools to calculate statistical power.

9. Explain what resampling methods are and why they are useful. Also explain their limitations.

Answer by Gregory Piatetsky:

Classical statistical parametric tests compare observed statistics to theoretical sampling distributions. Resampling a data-driven, not theory-driven methodology which is based upon repeated sampling within the same sample.

Resampling refers to methods for doing one of these
  • Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping)
  • Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests, randomization tests, or re-randomization tests)
  • Validating models by using random subsets (bootstrapping, cross validation)

See more in Wikipedia about bootstrapping, jackknifing.

See also How to Check Hypotheses with Bootstrap and Apache Spark
Bootstrap and Spark

Here is a good overview of Resampling Statistics.

10. Is it better to have too many false positives, or too many false negatives? Explain.

Answer by Devendra Desale.

It depends on the question as well as on the domain for which we are trying to solve the question.

In medical testing, false negatives may provide a falsely reassuring message to patients and physicians that disease is absent, when it is actually present. This sometimes leads to inappropriate or inadequate treatment of both the patient and their disease. So, it is desired to have too many false positive.

For spam filtering, a false positive occurs when spam filtering or spam blocking techniques wrongly classify a legitimate email message as spam and, as a result, interferes with its delivery. While most anti-spam tactics can block or filter a high percentage of unwanted emails, doing so without creating significant false-positive results is a much more demanding task. So, we prefer too many false negatives over many false positives.

11. What is selection bias, why is it important and how can you avoid it?

Answer by Matthew Mayo.

Selection bias, in general, is a problematic situation in which error is introduced due to a non-random population sample. For example, if a given sample of 100 test cases was made up of a 60/20/15/5 split of 4 classes which actually occurred in relatively equal numbers in the population, then a given model may make the false assumption that probability could be the determining predictive factor. Avoiding non-random samples is the best way to deal with bias; however, when this is impractical, techniques such as resampling, boosting, and weighting are strategies which can be introduced to help deal with the situation.

The second part of the answers will be published next week.