RA Statistical Analysis and Interpretation

Question 1

In hypothesis testing, a p-value of 0.03 indicates which of the following?

Accepted Answer

There is a 3% chance of observing the results if the null hypothesis is true

Answer

The probability of the null hypothesis being true is 3%

Answer

The effect size is statistically large

Answer

The alternative hypothesis has a 97% probability of being correct

Question 2

Which measure of central tendency is MOST affected by extreme outliers in a dataset?

Accepted Answer

Mean

Answer

Median

Answer

Mode

Answer

Range

Question 3

What is the primary distinction between descriptive and inferential statistics?

Accepted Answer

Descriptive statistics summarize a sample; inferential statistics draw conclusions about a population

Answer

Descriptive statistics use graphs; inferential statistics use tables

Answer

Descriptive statistics are qualitative; inferential statistics are quantitative

Answer

Descriptive statistics apply only to large samples; inferential to small samples

Question 4

A 95% confidence interval for a mean of 50 is (46, 54). How should this be correctly interpreted?

Accepted Answer

If the study were repeated many times, 95% of such intervals would contain the true population mean

Answer

There is a 95% probability the population mean is between 46 and 54

Answer

95% of individual data points fall between 46 and 54

Answer

The sample mean has a 95% chance of equaling 50

Question 5

Which statistical test is most appropriate to compare the means of two independent groups when the data is normally distributed?

Accepted Answer

Independent samples t-test

Answer

Chi-square test

Answer

Pearson correlation

Answer

Wilcoxon signed-rank test

Question 6

Standard deviation is best described as a measure of:

Accepted Answer

The spread or variability of data around the mean

Answer

The central value of a dataset

Answer

The difference between the highest and lowest values

Answer

The skewness of a distribution

Question 7

A Type I error in hypothesis testing occurs when a researcher:

Accepted Answer

Rejects a true null hypothesis

Answer

Fails to reject a false null hypothesis

Answer

Accepts the alternative hypothesis correctly

Answer

Calculates an incorrect effect size