BNAD 276 Lecture Notes - Lecture 12: Central Limit Theorem, Confidence Interval, Sampling Distribution

• School: University of Arizona
• Department: Business Administration
• Course: BNAD 276
• Professor: Umashankar

Document Summary

Confidence intervals: a range of values that, with a known degree of certainty, includes an unknown population characteristic, such as population mean. Estimating a value by providing two scores between which we believe the true value lies. Example: sample is 10,000 newborns, mean weight is 7 pounds. Guessing a single number as opposed to a range of numbers. As the number of measurements increases, the data becomes more stable and a better approximation of the true (theoretical) probability. As the number of observations (n) increases, or the number of times the experiment is performed increases, the estimate will become more accurate. The signal will become more clear (static cancels out) With only a few people, any little error becomes exaggerated. With many people, any little error is minimized. Sampling distribution: a theoretical probability distribution of the possible values of some sample statistic that would occur if we were to draw and infinite number of same-sized samples from a population.