Confidence intervals are vital statistical tools that are important to be used in determining required sample sizes, populations proportions. I see the tool beneficial in quality assurance. For e,g. you may have an estimated % of sales, you can test the confidence intervals by conducting a small sample and obtain a more accurate%. This statistical technique is great for predictions in elections, exams, and even in sales.
The quote that had the most significance to me is the To not look at the data is foolish, but to look at the data as having all the answers is even more foolish. It is a collision of new-school statistics and statisticians against old-school managers, coaches, and instructors. Neither side is right, neither is wrong; there is so much to be gained from listening to both sides.”—Tim Kurkjian, I’m Fascinated by Sacrifice Flies: Inside the Game We All Love(2016). Good point, what comes out of statistics are the numbers or formula that you apply, while it is your safe haven, it is also worth the time to ensure that you are using the right numbers, for instance , knowing the correct sample size, knowing the standard deviation etc.
Confidence Intervals is a range of values that is made up of sample data so the parameter for the population is likely to happen inside that range of a specific probability, this is known as the level of confidence. To put it another way, it is a way to show how confident we are in the data we are presenting and can present it a percentage.
With sample size, bigger is only better if bigger is needed. Sample size is an important consideration when using confidence intervals. Really one must keep three factors in mind: The measure of error that is acceptable, the level of confidence desired, and variation or dispersion of the population that one wants to examine. Small margin or error will mean you need a large sample size, and a lager margin of error will mean wider confidence intervals. When thinking about confidence level, one must keep in mind that the higher the level, the larger population that is needed. Finally, if one is working with a highly dispersed population one needs a large sample pollution, and the opposite is true with populations that are concentrated call for a smaller sample size.
I enjoyed the quote Mark Twain “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.” I think you can apply this one to people having misleading ideas that they perceive as absolute truths. This can be applied to statistics in that if the data, or the way in which it is measured, is compromised then is past as correct, it can lead to others to make poorly informed decision.
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