Statistical Inference

Statistical inference deals with the methods of drawing conclusions about the population characteristics on the basis of information contained in a sample drawn from the population. Population mean is not known to us, but we know the sample mean. In statistical inference we would be interested in answering two types of questions. First, what would be the value of the population mean? The answer lies in making an informed guess about the population mean. This aspect of statistical inference is called estimation. The second question pertains to certain assertion made about the population mean.

Suppose a manufacturer of electric bulbs claims that the mean life of electric bulbs is equal to 2000 hours. On the basis of the sample information, can we say whether the assertion is correct or not? This aspect of statistical inference is called hypothesis testing. We discuss these two aspects below.

Estimation

Estimation could be of two types: point estimation and interval estimation. In point estimation we estimate the value of the population parameter as a single point. On the other hand, in the case of interval estimation we estimate lower and upper bounds around sample mean within which population mean is likely to remain.

Hypothesis Testing

A hypothesis is a tentative statement about a characteristic of a population. It could be an assertion or a claim also. In hypothesis testing there are four important components: i) null hypothesis, ii) alternative hypothesis, iii) test statistic, and iv) interpretation of results.