Interview Questions and Answers

Example:

If you roll a fair six-sided die many times and calculate the average, the distribution of those averages will be approximately normal.

Example:

Predicting house prices based on factors like square footage, number of bedrooms, and location.

Example:

Testing whether a new drug has a significant effect by comparing the outcomes of treated and untreated groups.

Example:

There is a correlation between ice cream sales and drowning incidents, but one doesn't cause the other; both are influenced by warm weather.

Example:

A 95% confidence interval for the average height of a population is 160 to 170 cm.

Example:

Type I: Concluding a new drug is effective when it is not. Type II: Concluding a new drug is not effective when it is.

Example:

Comparing the average scores of students in three different teaching methods.

Example:

Conducting multiple tests on the same data until a significant result is found and then reporting only that result.

Example:

Correlation coefficient ranges from -1 to 1; covariance can take any value.

Example:

In a regression predicting house prices, if square footage and number of bedrooms are strongly correlated, multicollinearity may occur.

Example:

Checking if a set of exam scores follows a normal distribution using a Q-Q plot.

Example:

A study with a larger sample size generally has higher power to detect a true effect.

Example:

Testing if there is a significant association between gender and voting preference.

Example:

One-tailed test: Does a new drug increase performance? Two-tailed test: Does a new drug have any effect on performance?

Example:

If R-squared is 0.75, 75% of the variance in the dependent variable is explained by the independent variables.

Example:

A dataset with a long tail to the right has positive skewness.

Example:

Comparing the average scores of two groups of students who were taught using different methods.

Example:

In a dataset of exam scores, a score of 120 when others range from 50 to 100 may be an outlier.