![how to do one-mean confidence intervals on minitab 18 how to do one-mean confidence intervals on minitab 18](https://s3-us-west-2.amazonaws.com/courses-images/wp-content/uploads/sites/1888/2017/05/11170734/035_1_fmt.png)
If 99.9% of the parts sampled PASSED and the 0.1% FAILED, the chances of error are very low regardless of sample size. The accuracy of the CI also depends on the percentage of your sample that picks a particular answer. Click here for information regarding sampling plans. Sampling plans are an important step to ensure the data taken within is reflective and meaningful to 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. The CI calculations assume you have a true random sample of the population.
![how to do one-mean confidence intervals on minitab 18 how to do one-mean confidence intervals on minitab 18](https://www.mygeekytutor.com/images/minitab-basic-statistics_files/image044.gif)
The size of the population is a factor when working with a relatively small and known group of data (such as the number of pieces of candy in a bag versus the number of fish in the ocean). Though the relationships are not linear, the larger the sample size the smaller the confidence interval (in other words, the more confident you can be that it the true population parameters will fall within a tighter spectrum or tighter distribution). The larger your sample size, the more confidence one can be that their answers represent the population. There are three factors that impact the confidence interval:ĬI's are used when you are unable to capture and analyze an entire population (census) and the sample (statistics) to infer statements about a population. This CI is likely to contain the true best fit line. In regression analysis, the CI is based on a provided value of X for a given level of confidence. CI's are applied in statistical test for means, standard deviations, proportions, capability indices, regression analysis, and upper/lower control limits on control charts. A specified value of the CI signifies that probability of the interval containing the population parameter, and that there is an alpha-risk (1-CI) that the population parameter is not contained within the interval. Alpha-risk is known as the significance level the probability of being making an incorrect decision, in other words, being wrong. (1 - alpha-risk) is called the probability content or level of confidence. Confidence Interval = CI = 1 - alpha risk
![how to do one-mean confidence intervals on minitab 18 how to do one-mean confidence intervals on minitab 18](https://support.minitab.com/en-us/minitab/18/interval_plot_y_groups.png)
A 0.99 confidence interval states that there is 99% probability that the interval contains the population parameter, and that there is a 1.0% risk that the population parameter is not contained within the interval. Selecting a 99% CI suggests that approximately 99 out of 100 CI's will contain the population parameter. Confidence Intervals are used to quantify the uncertainty by providing a lower limit and upper limit that represent a range of values that will represent the true population parameter with a specified level of confidence. Sample statistics such as the mean, standard deviation and proportion (x-bar, s, p-bar) are only estimates of the population parameters.