The Dixon Q-test | When to Discard Outliers in Your Data

The video tutorial explains how to assess the precision of a data set using parts per thousand and identifies outliers through the Dixon Q test. It details the process of calculating variance and range, and how to compare Q experimental and Q critical values to determine if an outlier can be discarded. The tutorial also demonstrates repeating the Dixon Q test for additional data points.

What is the threshold for parts per thousand to determine good precision in a data set?

• 25.0

• 10.0

• 15.0

• 20.0

What is the first step in performing a Dixon Q test?

• List the data in decreasing order

• Identify the extreme value

• Calculate the standard deviation

• Calculate the mean of the data set

How is the variance calculated in the Dixon Q test?

• By subtracting the smallest value from the largest value

• By dividing the sum of all values by the number of values

• By subtracting the extreme value from the next closest value

• By adding all values and dividing by the number of values

What is the Q experimental value used for in the Dixon Q test?

• To calculate the mean of the data set

• To find the median of the data set

• To determine the range of the data set

• To compare against the Q critical value

What happens if the Q experimental is greater than the Q critical?

• The extreme value is rejected

• The data set is recalculated

• The test is repeated

• The extreme value is kept

What should be done if the Q experimental is less than the Q critical?

• Increase the number of data points

• Recalculate the Q experimental

• Keep the extreme value

• Reject the extreme value

What is the purpose of repeating the Dixon Q test?

• To calculate the standard deviation

• To find the median of the data set

• To check for additional outliers

• To confirm the mean of the data set