Understanding Inference

This lesson shares an overview of Sampling and Confidence Interval within the realm of statistical analysis. This is a very important lesson as the concepts discussed lie at the core of hypothesis testing. You will understand difference between a population and sample, types of sampling, sample distribution and sampling distribution of the mean, central distribution theorem, sampling error and introduction to confidence interval.

A statistic is a characteristic of a sample; a parameter is a characteristic of a population 

Population & Sample

Population are all items/people who are part of the statistical study. For example, height of people, average no. of smartphones per household in a city, state, country etc. Sample is a portion chosen from the population. In the video, population is infinite does not mean it cannot be calculated. However in many cases the population is estimated. Its difficult or highly improbable to measure all the stones in this universe, yet you know all securities in the S&P 500 Index that  can be a reference population for a research work.  To understand statistical inference from data, its key to understand sampling distribution of the sample mean. Refer to the video for an introduction to concepts mentioned above.  Under the material section, a solved numerical is given to build on the material covered.

The middle Road | YouTube 

The learnings of this section are crucial as they share an insightful understanding from the world of statistics. Designed to share fundamental concepts example confidence interval of 95 percent means 95 percent of all sample means are within +/- 1.96 standard errors from the population mean µ.

α level of significance here is 5 percent i.e. 1- α degree of confidence