A standardized test is used to assess students knowledge of world events (national reported mean=65, S=5). How do these students compare to the national sample? The confidence level is a measure of certainty regarding how accurately a sample reflects the population being studied within a chosen confidence interval. The result is called a confidence interval for the population mean, When the population standard deviation is known, the formula for a confidence interval (CI) for a population mean is deviation, n is the sample size, and z* represents the appropriate z *-value from the standard normal distribution for your desired confidence level. The confidence interval is the plus-or-minus figure usually reported in newspaper or television opinion poll results.For example, if you use a confidence interval of 4 and 47% percent of your sample picks an answer you can be “sure” that if you had asked the question of the entire relevant population between 43% (47-4) and 51% (47+4) would have picked that answer. In statistics, the term “Confidence Interval” refers to the range of values within which the true population value would lie in case of a sample out of the population. Interpreting Confidence Intervals. Compute a 99 percent confidence interval based on this sample's data. In other words, the confidence interval represents the amount of uncertainty expected while determining the sample population estimate or mean of a true population. This percentage represents how confident you are that the results will capture the true population parameter, depending on the luck of the draw with your random sample. A sample of 30 students are tested (sample mean=58, Standard Error=3.2). We obtain this estimate by using a simple random sample.From this sample, we calculate the statistic that corresponds to the … If the population standard deviation cannot be used, then the sample standard deviation, s, can be used when the sample size is greater than 30. The Form of a Confidence Interval . In statistics, every confidence interval (and every margin of error, for that matter) has a percentage associated with it, called a confidence level. Referring to our prior example of average emergency room wait time from our discussion on confidence intervals for a population mean, our by-hand calculations produced a 95% confidence interval of 24.28 to 35.72 minutes. 95 percent confidence interval: 26.66357 27.12504 sample estimates: mean of x 26.8943. The confidence interval can be expressed in terms of a single sample: "There is a 90% probability that the calculated confidence interval from some future experiment encompasses the true value of the population parameter." R defaults to computing a 95% confidence interval, but you can specify the confidence interval as follows: > t.test(bmi,conf.level=.90) This would compute a 90% confidence interval… The first part is the estimate of the population parameter. Calculating confidence intervals: Calculating a confidence interval involves determining the sample mean, X̄, and the population standard deviation, σ, if possible. Answer To interpret a confidence interval remember that the sample information is random - but there is a pattern to its behavior if we look at all possible samples. Recall the following for that example: sample size 50, sample mean 30, and sample standard deviation 20. Independent Samples Confidence Interval Calculator. 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