Questions et reponses d'entretien

Example:

Population: All students in a university. Sample: 100 students randomly selected from the university.

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If p-value is 0.05, there is a 5% chance of observing the results if the null hypothesis is true.

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For a set of test scores: 70, 72, 75, 78, 80. Standard deviation is approximately 3.16.

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Population parameter: Average income of all households. Sample statistic: Average income of a sample of 100 households.

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A 99% confidence interval for the average weight of a certain species of bird is 20 to 25 grams.

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Creating a histogram to visualize the distribution of exam scores in a class.

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Comparing the distribution of salaries in different departments using box plots.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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In a study tracking time to failure of light bulbs, if some bulbs are still functional at the end of the study, it is Type I censoring.

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Comparing the distributions of test scores between two different teaching methods when assumptions for a t-test are not met.

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A treatment is effective in both men and women, but when the data is combined, it seems ineffective due to differences in baseline characteristics.

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Choosing between linear and quadratic regression models based on AIC values.

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Creating multiple bootstrap samples from a dataset to estimate the uncertainty around the mean.