Inferential Statistical Methods for Energy Risk Managers

Unlike descriptive statistics, inferential statistics are procedures for determining whether it is possible to make generalizations based on the data collected from a sample. Such generalizations are about an unobserved population. A population consists of all values (past and future) of the random variable of interest. In most circumstances the exact value of a population parameter such as the mean or variance will be unknown, and we will have to make some conjecture about its true value. In Chapter 3, we used sample estimators such as the mean, median, skew, and kurtosis, to provide estimates of the respective population parameters. When a sample is drawn from a population, the evidence contained within it may bolster our conjecture about population values or it may indicate that the conjecture is untenable. Hypothesis testing is a formal mechanism by which we can make and test inferential statements about the characteristics of a population. It uses the information contained in a sample to assess the validity of a conjecture about a specific population parameter.