A statistician is analyzing a dataset with 1500 data points. If they decide to use a 95% confidence interval with a standard deviation of 30, what is the margin of error assuming a normal distribution?

Sat, 02 May 2026 16:17:08 +0000

**Why Accurate Confidence Estimation Matters in Data Analysis—And What It Says About Your Numbers** In an age where data shapes decisions across industries, even subtle details in statistical analysis carry weight. When a statistician works with a dataset containing 1,500 data points and a standard deviation of 30, one key metric determines how precisely their conclusions reflect the true population. That’s the margin of error—a measure of confidence in estimates drawn from sample data. For those seeking clarity amid the deluge of numbers, understanding how this margin is calculated reveals more than just math; it illuminates the reliability of insights powering businesses, research, and policy. ### What Is the Margin of Error, Really? The margin of error quantifies the uncertainty inherent in estimating a population parameter from a sample. Using a 95% confidence interval means the analyst is 95% sure the true value lies within a range centered on their sample statistic—within about ±1.96 standard errors. With a standard deviation of 30, this interval grows with data precision, revealing how much variation exists and how confidently conclusions can be drawn.