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The 95% confidence interval defines a range of values that you can be 95% certain contains the population mean. With large samples, you know that mean with much more precision than you do with a small sample, so the confidence interval is quite narrow when computed from a large sample.
A 90% confidence level means that we would expect 90% of the interval estimates to include the population parameter. Likewise, a 99% confidence level means that 95% of the intervals would include the parameter.
For example, a 99% confidence interval will be wider than a 95% confidence interval because to be more confident that the true population value falls within the interval we will need to allow more potential values within the interval. The confidence level most commonly adopted is 95%.
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). A 90 percent confidence interval would be narrower (plus or minus 2.5 percent, for example).
The bigger the confidence level, the wider the interval is. That's because a confidence level refers to the percentage of all possible samples that can be expected to include the true population parameter. A 95% confidence level implies that 95% of the confidence intervals would include the true population parameter.
A 90% confidence level means that we would expect 90% of the interval estimates to include the population parameter. Likewise, a 99% confidence level means that 95% of the intervals would include the parameter.
2:02 5:28 Suggested clip Calculating the Confidence interval for a mean using a formula YouTubeStart of suggested client of suggested clip Calculating the Confidence interval for a mean using a formula
For a population with unknown mean and known standard deviation, a confidence interval for the population mean, based on a simple random sample (SRS) of size n, is + z*, where z* is the upper (1-C)/2 critical value for the standard normal distribution.
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