1. Let's clarify the problem: You are asking if when calculating a z-value (often in statistics), you should use only sample data to get the most accurate answer. 2. The z-value formula for a sample is typically: $$z = \frac{\bar{x} - \mu}{\frac{\sigma}{\sqrt{n}}}$$ where $\bar{x}$ is the sample mean, $\mu$ is the population mean, $\sigma$ is the population standard deviation, and $n$ is the sample size. 3. Important rule: If the population standard deviation $\sigma$ is unknown, we usually use the sample standard deviation $s$ and a t-distribution instead of a z-distribution. 4. When calculating the z-value, you use the sample data (sample mean $\bar{x}$ and sample size $n$) because the population parameters are often unknown. 5. Using sample data allows you to estimate the z-value to understand how far your sample mean is from the population mean in terms of standard errors. 6. So yes, to find the z-value in practice, you rely on sample data to get the most accurate and relevant answer for your specific sample. Final answer: Use sample data (sample mean and sample size) to calculate the z-value when population parameters are unknown for the most accurate result.