In statistics, a confidence interval is a technique used to estimate the interval of an observed data that contain accurate data of unknown population parameter ( Roger & Ron, 2015) . The method has a confidence level that quantifies the standard of confidence that the existing settings lie in the interval. Precisely, confidence intervals come from a constant confidence level derived from an infinite number. When identifying the confidence interval the process needs to have a normal (Gaussian) distribution because inferences made are from individual observations and not the average ( Roger & Ron, 2015). Apple is a trusted company that has used the technique to gain several winning strategies. The method is used to calculate mean, variance and mean differences between apple and standard TVs.
The technique has helped the Apple production company to vary the best price for its customers. During the last calculation, the company accessed information on demand and price acceptance. The cost for a 42-inch television cost the customers $1500. Aftermarket analysis through the technique, the company discovered that only 12% of the market could make an order. After that, the company realized that the fair price of the product should range between $536 and $1500. In this case, the technique helped the company to analyze the strengths and weaknesses of its market. In the end, the company was able to come up with a reasonable price.
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A similar technique can be used in an educational project to evaluate the efficiency of its strategies. A project such as online discussion requires confidence interval technique to establish its effectiveness ( Xiao et al., 2019 ). The, in this case, will experience challenges related to efficiency compared to offline class discussion. In this case, it is essential to include a swift solution that consists of estimated data to evaluate its efficacy. However, the data should try to remain to stand and near the rough estimate.
References
Roger, H. & Ron, D.S. (2015). Statistical Thinking: Improving Business Performance. 2rd Edition, 307-329.
Xiao, H., Gao, J., Li, Q., Ma, F., Su, L., Feng, Y., & Zhang, A. (2019). Towards confidence interval estimation in truth discovery. IEEE Transactions on Knowledge and Data Engineering , 31 (3), 575-588.
