With a 95 percent confidence interval, you have a 5 percent chance of being wrong. With a 90 percent confidence interval, you have a 10 percent chance of being wrong. A 99 percent confidence interval would be wider than a 95 percent confidence interval (for example, plus or minus 4.5 percent instead of 3.5 percent).

Thus 68% of all sample means will be within one standard error of the population mean (and 95% within two standard errors). The smaller the standard error, the less the spread and the more likely it is that any sample mean is close to the population mean. A small standard error is thus a Good Thing.

The standard error is also inversely proportional to the sample size; the larger the sample size, the smaller the standard error because the statistic will approach the actual value.

The 95% confidence interval will be wider than the 90% interval, which in turn will be wider than the 80% interval. For example, compare Figure 4, which shows the expected value of the 80% confidence interval, with Figure 3 which is based on the 95% confidence interval.

1.96

Checking Out Statistical Confidence Interval Critical Values

Student’s T Critical Values

For example, if you want a t*-value for a 90% confidence interval when you have 9 degrees of freedom, go to the bottom of the table, find the column for 90%, and intersect it with the row for df = 9. This gives you a t*–value of 1.833 (rounded).

To find the critical value, follow these steps.

1. Compute alpha (α): α = 1 – (confidence level / 100)
2. Find the critical probability (p*): p* = 1 – α/2.
3. To express the critical value as a z-score, find the z-score having a cumulative probability equal to the critical probability (p*).