Next, we will dig little on “**Hypothesis testing”** – based on a collected sample we need to draw inferences or to make a conclusion about the data, we need hypothesis testing. In simpler words, the process of drawing inferences (making decisions) about the sample with regards to the population as a whole is known as hypothesis testing. To know more about it, you can visit our page Hypothesis Testing.

Now, let’s discuss the basic sample tests viz. one sample t-test and two-sample t-test.

**→ One sample t-test** – When we have one sample group, we will use one sample t-test.

**→ Two sample t-test** – When we have two independent sample groups, we will use two-sample t-test.

Suppose in a **pharmaceutical company**, QA officer collects a two sample groups (A and B) each of 10 tablets to measure the dissolution rate of a drug. We can compare which of these types have a better dissolution rate and hence we able to choose the best one. Here, we apply a two-sample t-test.

By now we know the basic concepts of sample tests, when we have one sample, we use one sample t-test and when we have two sample groups, we use two-sample t-test. Isn’t it simple?

In many real-life scenarios, we will have more than two sample groups say three or four sample groups then “What can we do?” In this case, **ANOVA** comes into play. Finally……right

ANOVA (known as Analysis of Variance) is a technique which is used to check whether the means of two or more sample groups are statistically different or not.

Suppose in the **healthcare industry**, we can use the ANOVA test to **compare different medications **and the effect on patients. If a company has **3 different medications **for treating ulcers and by using ANOVA, we can determine the effectiveness of treating them. We can compare which medication works better for treatment and hence we able to choose the best one.

During the vendor quality evaluation, we can use ANOVA to evaluate the quality of product supplies received from different vendors. Similarly, we can apply in various healthcare scenarios.

Reference

Suppose in a pharmaceutical company, QA officer collects four sample groups (A, B, C & D) each of 6 tablets to measure the hardness of a tablet.

From the plotted graph, we can conclude that the means are significantly different from each other. We can compare which of these types have a better hardness rate and hence we able to choose the best one.

Let’s discuss a few of the assumption it holds.

• Data must be numerical in nature.

• The sample data (variable) should be independent.

• Data must follow a normal distribution.

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