The mean is a measure of variability.

A histogram is a graphical representation of data.

The mode is the most frequently occurring value in a dataset.

The median is always equal to the mean in a dataset.

Standard deviation measures the spread of data around the mean.

Descriptive statistics allow us to make predictions about a population.

The range is the difference between the maximum and minimum values in a dataset.

A frequency distribution shows the number of occurrences of each data value.

Percentiles divide data into ten equal parts.

Variance is the square root of the standard deviation.

The mode can be used for both numerical and categorical data.

The sum of squared deviations from the mean is always zero.

Descriptive statistics involve probability and hypothesis testing.

If all data values are identical, the standard deviation is zero.

The mean is sensitive to extreme values (outliers).

Descriptive statistics only apply to numerical data.

Inferential statistics allow us to make inferences about a population based on a sample.

Hypothesis testing is used to assess the validity of a claim about a population.

A larger sample size increases the precision of the estimate of a population parameter.

The t-distribution is used when the population standard deviation is unknown.

The z-test is appropriate when the sample size is small and the population variance is unknown.

The chi-square test is used for testing relationships between categorical variables.

The standard error of the mean decreases as the sample size increases.

The alternative hypothesis is the hypothesis we are trying to prove in hypothesis testing.

Statistical significance always implies practical significance.

The F-test is used to compare two variances.