#1
1. In a standard deck of playing cards, what is the probability of drawing a heart?
#2
6. If the probability of event A is 0.4 and the probability of event B is 0.6, what is the probability of both events A and B occurring (assuming independence)?
#3
11. In a binomial distribution, what are the parameters 'n' and 'p'?
Mean and standard deviation
Number of trials and probability of success
Range and mode
Sample size and margin of error
#4
16. What is the formula for calculating the standard error of the mean?
Standard deviation / Square root of sample size
Square root of sample size / Standard deviation
Sample size / Standard deviation
Square root of sample size * Standard deviation
#5
21. What is the purpose of the F-test in analysis of variance (ANOVA)?
To compare variances of two independent samples
To compare means of two independent samples
To test the equality of means across multiple groups
To test the normality of the data
#6
2. What is the formula for calculating the mean (average) of a set of numbers?
Sum of numbers / Number of numbers
Number of numbers / Sum of numbers
Product of numbers / Number of numbers
Number of numbers / Product of numbers
#7
3. In a normal distribution, what percentage of data falls within one standard deviation of the mean?
#8
7. What is the purpose of a p-value in hypothesis testing?
To determine the effect size
To quantify the strength of the evidence against the null hypothesis
To establish causation
To calculate the margin of error
#9
8. If the variance of a dataset is 25, what is the standard deviation?
#10
12. What is the central limit theorem?
A statistical test for outliers
The law of large numbers
The distribution of sample means approaches a normal distribution as the sample size increases
A method for calculating confidence intervals
#11
13. In regression analysis, what does the coefficient of determination (R-squared) represent?
The slope of the regression line
The strength and direction of the relationship between variables
The proportion of the variance in the dependent variable explained by the independent variable(s)
The intercept of the regression line
#12
17. In a chi-squared test, what does the p-value indicate?
The strength of the relationship between variables
The probability of observing the data if the null hypothesis is true
The effect size of the test
The confidence level of the test
#13
4. What is the difference between correlation and causation in statistical analysis?
There is no difference, they are synonyms
Correlation implies causation
Correlation does not imply causation
Causation implies correlation
#14
5. What is the significance level typically set at in hypothesis testing?
#15
9. What is the formula for calculating the probability density function (PDF) of a continuous random variable?
f(x) = F(x) - F(x-1)
f(x) = dF(x)/dx
f(x) = F(x+1) - F(x-1)
f(x) = F(x+1) - F(x)
#16
10. In statistical terms, what does 'Type II error' refer to?
Incorrectly rejecting a true null hypothesis
Correctly rejecting a false null hypothesis
Incorrectly failing to reject a false null hypothesis
Correctly failing to reject a true null hypothesis
#17
14. What is the purpose of a confidence interval in statistics?
To set limits on the probability of a Type I error
To estimate the range within which the true population parameter is likely to fall
To determine the significance level of a hypothesis test
To calculate the margin of error
#18
15. What is the difference between a parametric and non-parametric statistical test?
Parametric tests assume a normal distribution, while non-parametric tests do not
Parametric tests do not require assumptions about the distribution, while non-parametric tests do
Parametric tests are only applicable to large sample sizes, while non-parametric tests are suitable for small samples
There is no difference, and the terms are used interchangeably
#19
19. What is the difference between a one-tailed and two-tailed hypothesis test?
One-tailed tests have a single critical region, while two-tailed tests have two critical regions
One-tailed tests are used for small sample sizes, while two-tailed tests are for large samples
There is no difference; the terms are used interchangeably
One-tailed tests are always more powerful than two-tailed tests