He/she can broadly follow the below steps: Select a group of people Record the individual eating time of a standard size burger Calculate the average eating time for the group Finally, compare that average value with the set value of 10 That, in a nutshell, is how we can perform a one-sample t-test.

Here’s the formula to calculate this: where, t = t-statistic m = mean of the group µ = theoretical value or population mean s = standard deviation of the group n = group size or sample size Note: As mentioned earlier in the assumptions that large sample size should be taken for the data to approach a normal distribution.

(Although t-test is essential for small samples as their distributions are non-normal).

Once we have calculated the t-statistic value, the next task is to compare it with the critical value of the t-test.

We can find this in the below t-test table against the degree of freedom (n-1) and the level of significance: This method helps us check whether the difference between the means is statistically significant or not.

Let’s further solidify our understanding of a one-sample t-test by performing it in R.

Implementing the One-Sample t-test in R A mobile manufacturing company has taken a sample of mobiles of the same model from the previous month’s data.

They want to check whether the average screen size of the sample differs from the desired length of 10 cm.

You can download the data here.

Step 1: First, import the data.

Step 2: Validate it for correctness in R: View the code on Gist.

Output: #Count of Rows and columns [1] 1000 1 > #View top 10 rows of the dataset Screen_size.

in.

cm.

1 10.

006692 2 10.

081624 3 10.

072873 4 9.

954496 5 9.

994093 6 9.

952208 7 9.

947936 8 9.

988184 9 9.

993365 10 10.

016660 Step 3: Remember the assumptions we discussed earlier?.We need to check them: View the code on Gist.

We get the below Q-Q plot: Almost all the values lie on the red line.

We can confidently say that the data follows a normal distribution.

Step 4: Conduct a one-sample t-test: View the code on Gist.

Output: One Sample t-test data: data$Screen_size.

in.

cm.

t = -0.

39548, df = 999, p-value = 0.

6926 alternative hypothesis: true mean is not equal to 10 95 percent confidence interval: 9.

996361 10.

002418 sample estimates: mean of x 9.

99939 The t-statistic comes out to be -0.

39548.

Note that we can treat negative values as their positive counterpart here.

Now, refer to the table mentioned earlier for the t-critical value.

The degree of freedom here is 999 and the confidence interval is 95%.

The t-critical value is 1.

962.

Since the t-statistic is less than the t-critical value, we fail to reject the null hypothesis and can conclude that the average screen size of the sample does not differ from 10 cm.

We can also verify this from the p-value, which is greater than 0.

05.

Therefore, we fail to reject the null hypothesis at a 95% confidence interval.

Independent Two-Sample t-test The two-sample t-test is used to compare the means of two different samples.

Let’s say we want to compare the average height of the male employees to the average height of the females.

Of course, the number of males and females should be equal for this comparison.

This is where a two-sample t-test is used.

Here’s the formula to calculate the t-statistic for a two-sample t-test: where, mA and mB are the means of two different samples nA and nB are the sample sizes S2 is an estimator of the common variance of two samples, such as: Here, the degree of freedom is nA + nB – 2.

We will follow the same logic we saw in a one-sample t-test to check if the average of one group is significantly different from another group.

That’s right – we will compare the calculated t-statistic with the t-critical value.

Let’s take an example of an independent two-sample t-test and solve it in R.

Implementing the Two-Sample t-test in R For this section, we will work with data about two samples of the various models of a mobile phone.

We want to check whether the mean screen size of sample 1 differs from the mean screen size of sample 2.

You can download the data here.

Step 1: Again, first import the data.

Step 2: Validate it for correctness in R: View the code on Gist.

Step 3: We need to check the assumptions as we did above.

I will leave that exercise up to you now.

Also, in this case, we will check the homogeneity of variance: View the code on Gist.

Output: #Homogeneity of variance > var(data$screensize_sample1) [1] 0.

00238283 > var(data$screensize_sample2) [1] 0.

002353585 Great, the variances are equal.

We can move ahead.

Step 4: Conduct the independent two-sample t-test: View the code on Gist.

Note: Rewrite the above code with “var.

equal = F” if you get unequal or unknown variances.

This will be a case of Welch’s t-test which is used to compare the means of two samples with unequal variances.

Output: Two Sample t-test data: data$screensize_sample1 and data$screensize_sample2 t = 1.

3072, df = 1998, p-value = 0.

1913 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -0.

001423145 0.

007113085 sample estimates: mean of x mean of y 10.

000976 9.

998131 What can you infer from the above output?.We can confirm that the t-statistic is again less than the t-critical value so we fail to reject the null hypothesis.

Hence, we can conclude that there is no difference between the mean screen size of both samples.

We can verify this again using the p-value.

It comes out to be greater than 0.

05, therefore we fail to reject the null hypothesis at a 95% confidence interval.

There is no difference between the mean of the two samples.

Paired Sample t-test The paired sample t-test is quite intriguing.

Here, we measure one group at two different times.

We compare separate means for a group at two different times or under two different conditions.

Confused?.Let me explain.

A certain manager realized that the productivity level of his employees was trending significantly downwards.

This manager decided to conduct a training program for all his employees with the aim of increasing their productivity levels.

How will the manager measure if the productivity levels increased?.It’s simple – just compare the productivity level of the employees before versus after the training program.

Here, we are comparing the same sample (the employees) at two different times (before and after the training).

This is an example of a paired t-test.

The formula to calculate the t-statistic for a paired t-test is: where, t = t-statistic m = mean of the group µ = theoretical value or population mean s = standard deviation of the group n = group size or sample size We can take the degree of freedom in this test as n – 1 since only one group is involved.

Now, let’s solve an example in R.

Implementing the Paired t-test in R The manager of a tyre manufacturing company wants to compare the rubber material for two lots of tyres.

One way to do this – check the difference between average kilometers covered by one lot of tyres until they wear out.

You can download the data from here.

Let’s do this!.Step 1: First, import the data.

Step 2: Validate it for correctness in R: View the code on Gist.

Step 3: We now check the assumptions just as we did in a one-sample t-test.

Again, I will leave this to you.

Step 4: Conduct the paired t-test: View the code on Gist.

Output: Paired t-test data: data$tyre_1 and data$tyre_2 t = -5.

2662, df = 24, p-value = 2.

121e-05 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -2201.

6929 -961.

8515 sample estimates: mean of the differences -1581.

772 You must be a pro at deciphering this output by now!.The p-value is less than 0.

05.

We can reject the null hypothesis at a 95% confidence interval and conclude that there is a significant difference between the means of tyres before and after the rubber material replacement.

The negative mean in the difference depicts that the average kilometers covered by tyre 2 are more than the average kilometers covered by tyre 1.

End Notes In this article, we learned about the concept of t-test, its assumptions, and also the three different types of t-tests with their implementations in R.

The t-test has both statistical significance as well as practical applications in the real world.

If you are new to statistics, want to cover your basics, and also want to get a start in data science, I recommend taking the Introduction to Data Science course.

It gives you a comprehensive overview of both descriptive and inferential statistics before diving into data science techniques.

Did you find this article useful?.Can you think of any other applications of the t-test?.Let me know in the comments section below and we can come up with more ideas!.You can also read this article on Analytics Vidhyas Android APP Share this:Click to share on LinkedIn (Opens in new window)Click to share on Facebook (Opens in new window)Click to share on Twitter (Opens in new window)Click to share on Pocket (Opens in new window)Click to share on Reddit (Opens in new window) Related Articles (adsbygoogle = window.

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