Example on Test of Significance

Some examples of test of significance are added below:

Example 1: T-Test for Medical Research – The T Test

For example, a medical study researching the performance of a new drug that comes to the conclusion of a reduced in blood pressure. The researchers predict that the patients taking the new drug will show a frankly larger decrease in blood pressure as opposed to the study participants on a placebo. They collect data from two groups: treat one group with an experimental drug and give all the placebo to the second group.

Researchers apply a t-test to the data in order determine the value of two assumed normal populations difference and study whether it statistically significant. The H0 (null hypothesis) could state that there is no significant difference in the blood pressure registered in the two groups of subjects, while the HA1 (alternative hypothesis) should be indicating the positivity of a significant difference. They can check whether or not the outcomes are significantly different by using the t-test, and therefore reduce the possibility of any confusing hypotheses.

Example 2: Chi-Square Analysis in Market Research

Think about the situation where you have to carry out a market research work to ascertain the link between customers satisfaction (comprised of satisfied satisfied or neutral scores) and their product preferences (the three products designated as Product A, Product B, and Product C). A chi-square test was used by the researchers to check whether they had a substantial association with the two categorical variables they were dealing with.

The H0 null hypothesis states customer satisfaction and product preferences are unrelated, the contrary to which H1 alternative hypothesis shows the customers’ satisfaction and product preferences are related. Thereby, the researchers will be able to execute the chi-square test on the gathered data and find out if the existed observations among customer satisfaction and product preferences are statistically significant by doing so. This allows us to make conclusions how the satisfaction degree of customers affects the market conception of goods for the target market.

Example 3: ANOVA in Educational Research

Think of a researcher whom is studying if there is any difference between various learning ways and their effect on students’ study achievements. HO represents the null hypothesis which asserts no differences in scores for the groups while the alternative hypothesis (HA) claims at least one group has a different mean. Via use Analysis of Variance (ANOVA), a researcher determines whether or not there is any statistically significant difference in performance hence, across the methods of teaching.

Example 4: Regression Analysis in Economics

In an economic study, researchers examine the connection between ads cost and revenue for the group of businesses that have recently disclosed their financial results. The null space proposes that there is no such linear connection between the advertisement spending and purchases.

Among the models, the regression analysis used to determine whether the changes in sales are attributed to the changes in advertising to a statistically significant level (the regression line slope is significantly different from zero) is chosen.

Example 5: Paired T-Test in Psychology

A psychologist decides to do a study to find out if a new type of therapy can make someone get rid of anxiety. Patients are evaluated of their level of anxiety prior to initiating the intervention and right after.

The null hypothesis claims that there is no noticeable difference in the levels of anxiety from a pre-intervention to a post-intervention setting. Using a paired t-test, a psychologist who collected the anxiety scores of a group before and after the experiment can prove statistically the observed change in these scores.

Test of significance is a process for comparing observed data with a claim(also called a hypothesis), the truth of which is being assessed in further analysis. Let’s learn about test of significance, null hypothesis and Significance testing below.