HYPOTHESIS TESTING |L-6|ECONOMETRICS

Hypothesis Testing

Hypothesis testing refers to a formal process of investigating a supposition or statement to accept or reject it. The econometricians examine a random sample from the population. If it is consistent with the hypothesis, it is accepted. Otherwise it is rejected.

Types of Hypothesis

There are two types of hypothesis – Null and Alternative.

Null Hypothesis: It is denoted by H0. A null hypothesis is the one in which sample observations result purely from chance. This means that the observations are not influenced by some non-random cause.

Alternative Hypothesis: It is denoted by Ha or H1. An alternative hypothesis is the one in which sample observations are influenced by some non-random cause.

A hypothesis test concludes whether to reject the null hypothesis and accept the alternative hypothesis or to fail to reject the null hypothesis. The decision is based on the value of X and R.

Points to be noted:

Rejection implies that the null hypothesis is not true and alternative hypothesis can be accepted.

Fail to reject implies that there are no sufficient reasons to completely reject the null hypothesis or prefer alternative hypothesis over null hypothesis.

Decision Errors in Hypothesis Testing

Before we jump onto the process of hypothesis testing, let’s learn about the errors that can result from it. The errors are divided into two categories:

Type I Error: Type I error occurs when null hypothesis is rejected even when it’s true. This error leads to a conclusion that a relationship or a supposed effect exists when in reality it doesn’t.

The probability of committing Type I error is called alpha and is denoted by α.

This probability is called the significance level.

Also known as the error of first kind.

It’s a focus of skepticism

Examples:

A fair and balanced coin should result in equal heads and tails when flipped. Let’s say a coin is flipped 20 times, it will result in 10 heads and 10 tails while in fact it may not.

An experiment showing that a medical treatment is curing a disease when actually it is not.