A t-test is an inferential statistical method used to determine if there is a significant difference between the means of two groups. It requires the mean difference, the standard deviation of each group, and the number of data values, and several types exist, including one-sample, independent two-sample, and paired sample t-tests. Assumptions for conducting a t-test include normality of data distribution, random selection of observations, and equal variances among groups for independent tests.

1. Swipe
T-Test

2. A t-test is a type of inferential statistic used to
determine if there is a significant difference
between the means of two groups, which may be
related in certain features.
It is mostly used when the data sets, like the data
set recorded as the outcome from flipping a coin
100 times, would follow a normal distribution and
may have unknown variances.
T-Test

3. A t-test is used as a hypothesis testing tool, which
allows testing of an assumption applicable to a
population.
A t-test looks at the t-statistic, the t-distribution
values, and the degrees of freedom to determine
the statistical significance.
To conduct a test with three or more means, one
must use an analysis of variance.
T-Test

4. A t-test is a type of inferential statistic used to
determine if there is a significant difference
between the means of two groups, which may be
related in certain features.
The t-test is one of many tests used for the
purpose of hypothesis testing in statistics.
Key takeways

5. Calculating a t-test requires three key data values.
They include the difference between the mean
values from each data set (called the mean
difference), the standard deviation of each group,
and the number of data values of each group.
There are several different types of t-test that can
be performed depending on the data and type of
analysis required.

6. The data should follow a continuous or ordinal
scale (the IQ test scores of students, for example)
The observations in the data should be randomly
selected
The data should resemble a bell-shaped curve
when we plot it, i.e., it should be normally
distributed. You can refer to this article to get a
better understanding of the normal distribution
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)
Variances among the groups should be equal (for
independent two-sample t-test)
Assumptions for Performing a t-test

7. One sample t-test
Independent two-sample t-test
Paired sample t-test
Types of t-tests

8. The one-sample t-test is a statistical hypothesis
test used to determine whether an unknown
population mean is different from a specific value.
One sample t-test

9. The two-sample t-test (also known as the
independent samples t-test) is a method used to
test whether the unknown population means of
two groups are equal or not.
Independent two-sample t-test

10. The paired sample t-test, sometimes called the
dependent sample t-test, is a statistical procedure
used to determine whether the mean difference
between two sets of observations is zero. In a
paired sample t-test, each subject or entity is
measured twice, resulting in pairs of observations
Paired sample t-test

11. Chi-square Test
Non-Probability methods
Sentimental Analysis
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