CDF Interview Questions
CDF interview prep for cumulative probabilities, interval probabilities, transformations, maxima, minima, and inequality-direction mistakes.
Candidates practicing maxima, minima, transformations, and tail calculations.
CDF definition
A cumulative distribution function gives P(X <= x), the probability that the variable is at or below a threshold.
Interval probabilities
CDF values can produce interval probabilities. For example, P(a < X <= b) is F(b) - F(a) in a continuous setting.
Concrete example
If X is uniform on [0, 1], then F(0.7) = 0.7. The probability X is between 0.4 and 0.7 is 0.3.
Max and min use
CDF reasoning is especially useful for maxima and minima because events like max <= x or min > x often simplify.
Transformation use
For transformed variables, compute the CDF of the new variable by translating its inequality back to the original variable.
Common mistakes
Candidates often reverse the inequality or confuse F(x) with P(X = x). Say the event in words before calculating.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.