Converting observations into $z$-scores is also called standardizing the observations.

• A. normalizing
• B. standardizing
• C. transforming
• D. scaling

Answer: (B)

Verified step by step guidance

1. Understand that converting observations into z-scores involves subtracting the mean of the data from each observation and then dividing by the standard deviation of the data.
2. Recall the formula for a z-score: \(z = \frac{X - \mu}{\sigma}\), where \(X\) is the observation, \(\mu\) is the mean, and \(\sigma\) is the standard deviation.
3. Recognize that this process adjusts the data to have a mean of 0 and a standard deviation of 1, which is why it is called standardizing.
4. Note that 'normalizing' and 'scaling' can refer to other types of data transformations, but the specific term for converting to z-scores is 'standardizing'.
5. Conclude that the correct term for converting observations into z-scores is 'standardizing' because it standardizes the distribution of the data.