What term refers to the difference between a sample statistic and the population parameter?

What term refers to the difference between a sample statistic and the population parameter?

• A. Sampling Error
• B. Central Limit Theorem
• C. Normal Distribution
• D. Probability

Answer: (A)

The difference between a sample statistic and the population parameter is called sampling error. This gap appears because a sample only captures part of the population, so the estimate (like a sample mean or proportion) may be a bit high or a bit low relative to the true population value. It’s a random fluctuation that varies from sample to sample, and it typically gets smaller as the sample size grows because larger samples tend to reflect the population more closely. This idea is distinct from bias, which would be a systematic over- or under-estimate due to how the sample is chosen. The other terms are about different ideas: the Central Limit Theorem describes how the distribution of sample statistics behaves across many samples, the normal distribution is a specific bell-shaped model for data, and probability concerns how likely events are.

The difference between a sample statistic and the population parameter is called sampling error. This gap appears because a sample only captures part of the population, so the estimate (like a sample mean or proportion) may be a bit high or a bit low relative to the true population value. It’s a random fluctuation that varies from sample to sample, and it typically gets smaller as the sample size grows because larger samples tend to reflect the population more closely. This idea is distinct from bias, which would be a systematic over- or under-estimate due to how the sample is chosen.

The other terms are about different ideas: the Central Limit Theorem describes how the distribution of sample statistics behaves across many samples, the normal distribution is a specific bell-shaped model for data, and probability concerns how likely events are.