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Strictly speaking a 95% confidence interval means that if we were to take 100 different samples and compute a 95% confidence interval for each sample, then approximately 95 of the 100 confidence intervals will contain the true mean value (μ). In the health-related publications a 95% confidence interval is most often used, but this is an arbitrary value, and other confidence levels can be selected. The following table includes 95% confidence intervals for each characteristic, computed using the same formula we used for the confidence interval for the difference in mean systolic blood pressures. A single sample of participants and each participant is measured twice, once before and then after an intervention.
Objective:This section will explain the meaning of the Confidence Interval (CI) in statistical analysis. The CI states that with 92% confidence, the proportion of all similar companies with the plan will between 46% and 56%.
This means that there is a 95% probability that the confidence interval will contain the true population mean. When constructing confidence intervals for the risk difference, the convention is to call the exposed or treated group 1 and the unexposed or untreated group 2. Because the 95% confidence interval includes zero, we conclude that the difference in prevalent CVD between smokers and non-smokers is not statistically significant. A confidence interval for the difference in prevalent CVD (or prevalence difference) between smokers and non-smokers is given below. First, a confidence interval is generated for Ln(RR), and then the antilog of the upper and lower limits of the confidence interval for Ln(RR) are computed to give the upper and lower limits of the confidence interval for the RR.
If the confidence interval does not include the null value, then we conclude that there is a statistically significant difference between the groups. It is easier to be sure of extreme answers than those that are in the middle thus the interval is not linear. Suppose we want to generate a 95% confidence interval estimate for an unknown population mean. Suppose we compute a 95% confidence interval for the true systolic blood pressure using data in the subsample. The confidence interval for the difference in means provides an estimate of the absolute difference in means of the outcome variable of interest between the comparison groups. A goal of these studies might be to compare the mean scores measured before and after the intervention, or to compare the mean scores obtained with the two conditions in a crossover study.
In practice, however, we select one random sample and generate one confidence interval, which may or may not contain the true mean.

Note that for a given sample, the 99% confidence interval would be wider than the 95% confidence interval, because it allows one to be more confident that the unknown population parameter is contained within the interval. However, the natural log (Ln) of the sample RR, is approximately normally distributed and is used to produce the confidence interval for the relative risk.
Since the 95% confidence interval does not include the null value (RR=1), the finding is statistically significant. The larger your sample size, the more confidence one can be that their answers represent the population. If the sample is not then one cannot rely on the confidence intervals calculated, because you can no longer rely on the measures of central tendency and dispersion.Sampling plans are an important step to ensure the data taken within is reflective and meaningful to represent the population.
This formula is appropriate for large samples, defined as at least 5 successes and at least 5 failures in the sample. Suppose we wish to construct a 95% confidence interval for the difference in mean systolic blood pressures between men and women using these data. We can now use these descriptive statistics to compute a 95% confidence interval for the mean difference in systolic blood pressures in the population.
The margin of error is very small (the confidence interval is narrow), because the sample size is large.
The formulas for confidence intervals for the population mean depend on the sample size and are given below.
Then compute the 95% confidence interval for the relative risk, and interpret your findings in words.
In contrast, when comparing two independent samples in this fashion the confidence interval provides a range of values for the difference.
Another way of thinking about a confidence interval is that it is the range of likely values of the parameter (defined as the point estimate + margin of error) with a specified level of confidence (which is similar to a probability).
The confidence interval will be computed using either the Z or t distribution for the selected confidence level and the standard error of the point estimate.
The previous section dealt with confidence intervals for the difference in means between two independent groups. Note that this formula is appropriate for large samples (at least 5 successes and at least 5 failures in each sample).
Because the sample size is small, we must now use the confidence interval formula that involves t rather than Z. The men have higher mean values on each of the other characteristics considered (indicated by the positive confidence intervals).

For proportions, the normal distribution approximates the binomial for n x P(hat) is greater than or equal to 5.Most common confidence interval selections are 90%, 95%, or 99% but are dependent on the voice of the customer, your company, project, and other factors. If a 95% confidence interval includes the null value, then there is no statistically meaningful or statistically significant difference between the groups.
For each of the characteristics in the table above there is a statistically significant difference in means between men and women, because none of the confidence intervals include the null value, zero.
Suppose we want to compare mean systolic blood pressures in men versus women using a 95% confidence interval.
Compute the 95% confidence interval for the difference in proportions of patients reporting relief (in this case a risk difference, since it is a difference in cumulative incidence). The sample is large (> 30 for both men and women), so we can use the confidence interval formula with Z. Because the 95% confidence interval for the mean difference does not include zero, we can conclude that there is a statistically significant difference (in this case a significant improvement) in depressive symptom scores after taking the new drug as compared to placebo. The appropriate formula for the confidence interval for the mean difference depends on the sample size. Based on this interval, we also conclude that there is no statistically significant difference in mean systolic blood pressures between men and women, because the 95% confidence interval includes the null value, zero. Since the interval contains zero (no difference), we do not have sufficient evidence to conclude that there is a difference. The 95% confidence interval estimate for the relative risk is computed using the two step procedure outlined above.
Next we substitute the Z score for 95% confidence, Sp=19, the sample means, and the sample sizes into the equation for the confidence interval. Note also that this 95% confidence interval for the difference in mean blood pressures is much wider here than the one based on the full sample derived in the previous example, because the very small sample size produces a very imprecise estimate of the difference in mean systolic blood pressures. The risk ratio is a good measure of the strength of an effect, while the risk difference is a better measure of the public health impact, because it compares the difference in absolute risk and, therefore provides an indication of how many people might benefit from an intervention.
Confidence interval estimates for the risk difference, the relative risk and the odds ratio are described below.