Which term describes a distribution transformed to z-scores?

Which term describes a distribution transformed to z-scores?

• A. Standardized Distribution
• B. Probability
• C. Normal Distribution
• D. Unit Normal Table

Answer: (A)

When you convert values to z-scores, you’re centering the data at the mean and scaling by the standard deviation. This process is called standardizing, and the resulting set of scores is described as a standardized distribution. The z-score for a value shows how many standard deviations it is from the mean, via z = (X − μ)/σ (or z = (X − x̄)/s for a sample). If the original data were normal, these z-scores follow the standard normal distribution (mean 0, sd 1); otherwise, you still obtain a standardized distribution with those standardized units. The other options aren’t right: probability is about likelihood, not a distribution; the normal distribution is a specific bell-shaped form (not the act of standardizing); and the unit normal table is a lookup resource for probabilities, not the distribution itself.

When you convert values to z-scores, you’re centering the data at the mean and scaling by the standard deviation. This process is called standardizing, and the resulting set of scores is described as a standardized distribution. The z-score for a value shows how many standard deviations it is from the mean, via z = (X − μ)/σ (or z = (X − x̄)/s for a sample). If the original data were normal, these z-scores follow the standard normal distribution (mean 0, sd 1); otherwise, you still obtain a standardized distribution with those standardized units. The other options aren’t right: probability is about likelihood, not a distribution; the normal distribution is a specific bell-shaped form (not the act of standardizing); and the unit normal table is a lookup resource for probabilities, not the distribution itself.