The video tutorial covers measures of central tendency, focusing on mean, mode, and median. It explains how to calculate each measure and discusses their characteristics, including how they are affected by changes in data and outliers. The tutorial includes a class activity to reinforce understanding and highlights the limitations of using these measures with categorical data.

What is the process to calculate the mean of a data set?

• Add all numbers and divide by the total count

• Find the middle number

• Identify the most frequent number

• Subtract the smallest number from the largest

Which of the following best describes the mode?

• The average of all numbers

• The middle number in a sorted list

• The most frequently occurring number

• The difference between the highest and lowest numbers

How do you find the median in a data set with an even number of values?

• Subtract the smallest number from the largest

• Select the most frequent number

• Calculate the average of the two middle numbers

• Choose the middle number

Which measure of central tendency is least affected by small changes in the data set?

• Mean

• Mode

• Range

• Median

What happens to the mean when an outlier is introduced?

• It remains unchanged

• It becomes the median

• It becomes the mode

• It is significantly affected

Why can't the mean be used for categorical data?

• It is the same as the median

• It is the same as the mode

• It is always zero

• Categorical data has no numerical meaning

Which measure is completely unaffected by outliers?

• Mean

• Mode

• Median

• Range

What is a characteristic of categorical data?

• It can be averaged

• It is always quantitative

• It is either ordinal or nominal

• It has numerical meaning

Which measure of central tendency is usually unaffected by outliers?

• Mean

• Mode

• Median

• Range

What is the primary difference between ordinal and nominal data?

• Ordinal data has a meaningful order

• Nominal data can be averaged

• Ordinal data is always numerical

• Nominal data is always quantitative