The Independent Samples T-Test Method and How it Benefits Organizations

Kartik-Patel-CEO.jpg

Click to learn more about author Kartik Patel.

The
independent sample t-test is a statistical method of hypothesis testing that
determines whether there is a statistically significant difference between the
means of two independent samples.

For
example, one might use this method of analysis to determine whether the average
value of a sedan type of car is significantly different from an SUV type of car.
Here the hypothesis would be set as a null hypothesis: SUV and Sedan car types
have insignificant differences in terms of value, and the alternative
hypothesis value of SUV and sedan differ significantly.

Let’s look at a sample of the independent t-test on two variables. One is a dimension containing two values, and the other is a measure.

Image Source: Elegant MicroWeb
  • At a 95
    percent confidence level (5 percent chance of error): As p-value = 0.041,
    which is less than 0.05, there is a statistically significant difference
    between the means of the two groups A and B. The mean of Group A is
    significantly higher than that of Group B.
  • At a 98 percent
    confidence level (2 percent chance of error): As p-value = 0.041, which is
    greater than 0.02, there is no statistically significant difference
    between the means of the two groups A and B.

How
Can the Independent Samples T-Test Method Benefit an Organization?

The independent samples t-test method of hypothesis testing can be used to address various needs in various types of industries and organizations.

  • Medicine: Has the quality of life
    improved for patients who took drug A as opposed to patients who took drug
    B?
  • Sociology: Are men more satisfied with
    their jobs than women? Do they earn more?
  • Biology: Are foxes in one specific
    habitat larger than in another?
  • Economics: Is the economic growth of
    developing nations larger than the economic growth of the first world?
  • Marketing: Does customer segment A spend
    more on groceries than customer segment B?

To better understand the benefits of the independent sample t-test analysis, let’s look at two use cases:

Use Case 1

Business
Problem:
 An
HR manager wants to find out whether male employees earn more than female
employees. Here, the dependent variable would be “Total Annual Income.”

Business
Benefit:
 Once
the test is completed, a p-value is generated, which indicates whether there is
a statistical difference between the income of the two groups. Based on this
value, a manager can easily conclude whether the average income earned by
female employees is statistically different from male employees. If the
difference is statistically significant, they can further conclude which gender
earns higher or lower salaries.

Use Case 2

Business
Problem:
 A
grocery store sales manager wants to know whether customer segment A spends more
on groceries than customer segment B. Here, the dependent variable would be “Purchase
Amount.”

Business
Benefit:
 Once
the test is completed, a p-value is generated, which indicates whether there is
a statistical difference between the purchase amounts of both segments. Based
on this value, the grocery store manager can decide on marketing strategies for
better sales and increased revenue.

The
independent sample t-test is a useful statistical method of hypothesis testing
when an organization wants to determine whether there is a statistical
difference between two categories, groups, or items and, furthermore, if there
is a statistical difference, whether that difference is significant.

Credit: Source link