MGCR 271 Chapter Notes - Chapter 4: Statistical Inference, Sampling Distribution, Sample Space

• School: McGill University
• Department: Management Core
• Course: MGCR 271
• Professor: Glenn Zabowski

Document Summary

Statistical inference uses data to draw conclusions about the population or process from which the data come. The conclusion of inferences includes a statement, in the language of probability, about how reliable they are. The state(cid:373)e(cid:374)t gi(cid:448)es a p(cid:396)o(cid:271)a(cid:271)ility that a(cid:374)s(cid:449)e(cid:396)s the (cid:395)uestio(cid:374) (cid:862)what (cid:449)ould happe(cid:374) if i used this (cid:373)ethod (cid:448)e(cid:396)y (cid:373)a(cid:374)y ti(cid:373)es? (cid:863) The probabilities we need come from the sampling distributions of sample statistics. To find the sampling distribution of : keep taking random samples of size from a population with mean , find the sample mean for each sample, collect all the "s and display their distribution. To think more effectively about sampling distributions, we use the language of probability. Probability, the mathematics that describes randomness, is important in many areas. We concentrate on informal probability as the conceptual foundation for statistical inference. Because random samples and randomized comparative experiments use chance, their results vary according to the laws of probability.