MPSD Quiz Prep 1

What is the primary purpose of inferential statistics?

• Describe data trends

• Summarize data without drawing conclusions

• Make inferences about a population based on a sample

• Compare two or more descriptive statistics

Answer explanation

Inferential statistics use sample data to make predictions or generalizations about a larger population.

In hypothesis testing, what is the null hypothesis (H₀)?

• It assumes no effect or relationship exists in the population

• It predicts a significant effect in the population

• It is always accepted if the p-value is greater than 0.05

• It is the statement the researcher wants to prove

Answer explanation

The null hypothesis states there is no change, difference, or effect in the population.

Which of the following is a key assumption of the normal distribution?

• The mean, median, and mode are the same

• The distribution is skewed positively

• The majority of values fall within 1 standard deviation

• Outliers are common in a normal distribution

Answer explanation

A normal distribution is symmetrical, with its central tendency measures equal.

Given a dataset with μ = 50 and σ = 10, what is the z-score of a data point X = 65?

• 1.5

• 2.0

• 0.5

• -1.5

Answer explanation

If a study has a sample size of 100 and the population standard deviation is 15, what is the standard error (SE)?

• 1.5

• 3.0

• 5.0

• 1.0

Answer explanation

The larger the sample size, the smaller the standard error.

• TRUE

• FALSE

Answer explanation

The standard error (SE) measures the dispersion of sample means around the population mean. It is calculated as the population standard deviation (σ) divided by the square root of the sample size (n). As the sample size increases, the denominator grows, leading to a smaller SE. This indicates that larger samples provide more precise estimates of the population mean.

A Type I error occurs when we fail to reject a false null hypothesis.

• TRUE

• FALSE

Answer explanation

A Type I error occurs when we reject a true null hypothesis, leading to a false positive conclusion. Conversely, failing to reject a false null hypothesis results in a Type II error, which is a false negative.

A hypothesis test with a p-value of 0.03 means there is strong evidence to reject H₀ at α = 0.05.

• TRUE

• FALSE

Answer explanation

The p-value represents the probability of obtaining results at least as extreme as the observed ones, assuming the null hypothesis (H₀) is true. A p-value of 0.03 is less than the common significance level (α) of 0.05, indicating sufficient evidence to reject H₀.

The probability of making a Type II error is increased when the sample size is small.

• TRUE

• FALSE

Answer explanation

A Type II error occurs when we fail to reject a false null hypothesis. Smaller sample sizes often lack the statistical power to detect true effects, increasing the likelihood of Type II errors.

A z-score tells us how many standard deviations a value is from the mean.

• TRUE

• FALSE

Answer explanation

A z-score indicates the number of standard deviations a data point is from the population mean.