Exam 2 Review: Mean, Median, Mode, and Inferential Statistics

These flashcards cover key concepts from the lecture on mean, median, mode, and inferential statistics, along with their definitions and explanations.

What is the mean in statistics?

The average of a set of values.

What does median represent in a dataset?

The middle value of a distribution.

What is mode in statistics?

The most frequent value in a dataset.

Why is median preferred over mean in skewed distributions?

Median is not affected by extreme values, making it a better measure of central tendency.

What does a positive skew indicate about household income?

There are a few high-income outliers that increase the mean above the median.

What are the three key concepts of inferential statistics mentioned?

Normal distribution, hypothesis testing, significance testing.

What does a normal distribution suggest about the data?

More cases aggregate near the average.

Why is normal distribution important in statistics?

It gives predictability of the data and allows us to determine the percentage of cases between any two scores.

What percentage of data lies within plus or minus one standard deviation of the mean in a normal distribution?

68%.

What percentage of data lies within plus or minus two standard deviations of the mean in a normal distribution?

95%.

What is the Central Limit Theorem?

The averages of samples tend to have approximately normal distributions as sample size increases.

What does a small p-value indicate in significance testing?

There is a low probability that the observed difference/relationship is due to chance.

What is the threshold for a p-value to be considered significant?

Typically less than 0.05.

What does the independent samples t-test compare?

It compares the means of two different groups.

What does the T value in an independent samples t-test indicate?

It indicates how big the difference between two groups is.

How is the T value calculated?

By dividing the group difference (numerator) by within-group variability (denominator).