Confidence Frames

First we as what are confidence intervals? It is explained as a range of values so defined that there is a specified probability that the value of a parameter lies within it. The next question to be asked is how we form a confidence interval. The purpose of taking a random sample from a lot or population and calculating a statistic, such as the mean from the data, is to estimate the mean of the population. How well the sample statistic estimates the underlying population value is always an issue. A confidence interval addresses this issue because it provides a range of values which is likely to contain the population parameter of interest. Confidence intervals serve as good estimates of the population parameter because the procedure tends to
produce intervals that contain the parameter. Confidence intervals are comprised of the point estimate and a margin of error around that point estimate. The margin of error indicates the amount of uncertainty that surrounds the sample estimate of the population parameter. You can use either P values or confidence intervals to determine whether your results are statistically significant. If a hypothesis test produces both, these results will agree. In statistical analyses, there tends to be a greater focus on P values and simply detecting a significant effect or difference. However, a statistically significant effect is not necessarily meaningful in the real world. For instance, the effect might be too small to be of any practical value. It’s important to pay attention to the both the magnitude and the precision of the estimated effect.
That’s why I'm rather fond of confidence intervals. They allow you to assess these important characteristics along with the statistical significance. You'd like to see a narrow confidence interval where the entire range represents an effect that is meaningful in the real world. Many companies in different fields use this method and seem to have much success using it to comb thought significant amounts of data. My research has shown me that confidence intervals for the most part are misunderstood and because of that misunderstanding they are misused misinterpreted. With this method of testing I believe the pros outweigh the cons. Imagine that you are trying to find out how many Americans have taken at least two weeks of vacation in the past year. You could ask every American about his or her vacation schedule to get the answer, but this would be expensive and time consuming. To save time and money, you would probably survey a smaller group of American’s. However, your finding may be different from the actual value if you had surveyed the whole population. That is, it would be an estimate. Each time you repeat the survey, you would likely get slightly different
results. Commonly, when researchers present this type of estimate, they will put a confidence interval around it. The CI is a range of values, above and below a finding, in which the actual value is likely to fall. The confidence interval represents the accuracy or precision of an estimate.