Here are the differences between a t-test, a chi-square test and other statistical tests, plus when you should use them.
Written by Vibhor Nigam
For a person without a background in statistics, it can be difficult to understand the difference between fundamental statistical tests —not to mention when to use them. Below we cover the differences between the most common tests, how to use null value hypotheses in these tests and the conditions under which you should use each particular test.
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Learn about the t-test, the chi square test the p-value and moreBefore we learn about the tests, let’s dive into some key terms.
The null hypothesis proposes that no significant difference exists between a set of given observations.
To reject a null hypothesis, one needs to calculate test statistics, then compare the result with the critical value. If the test statistic is greater than the critical value, we can reject the null hypothesis.
A critical value is a point (or points) on the scale of the test statistic beyond which we reject the null hypothesis. We derive the level of significance ( α ) of the test.
Critical value can tell us the probability of two sample means belonging to the same distribution. The higher the critical value means the lower the probability of two samples belonging to the same distribution.
The general critical value for a two-tailed test is 1.96, which is based on the fact that 95 percent of the area of a normal distribution is within 1.96 standard deviations of the mean.
Critical values can be used to do hypothesis testing in the following ways:
If the test statistic is lower than the critical value, accept the null hypothesis; otherwise reject it.
Learn more about calculating a critical value:
Critical value (z*) for a given confidence level. | Video: Khan AcademyNote: Some statisticians would use p-value instead of critical value for conducting null hypothesis.
P-value is the probability to the right of the respective statistic (z, t or chi). The benefit of using p-value is that it calculates a probability estimate, which means we can test at any desired level of significance by comparing this probability directly with the significance level.
For example, assume the z-value for a particular experiment comes out to be 1.67 which is greater than the critical value at five percent (1.64). Now, to check for a different significance level of one percent, we calculate a new critical value.
However, if we calculate p-value for 1.67 and it comes to be 0.047, we can use this p-value to reject the hypothesis at a five percent significance level since 0.047 < 0.05. However, with a more stringent significance level of one percent, we’ll fail to reject the hypothesis since 0.047 >0.01. It’s important to note here that there’s no double calculation required.
In statistics, population refers to the total set of observations we can make. For example, if we want to calculate the average human height, the population will be the total number of people actually present on Earth.
A sample, on the other hand, is a set of data collected or selected from a predefined procedure. For our example above, a sample is a small group of people selected randomly from different regions of the globe.
To draw inferences from a sample and validate a hypothesis, the sample must be random.
For instance, if we select people randomly from all regions on Earth, we can assume our sample mean is close to the population mean, whereas if we make a selection just from the United States, then our average height estimate/sample mean cannot be considered close to the population mean. Instead, it will only represent the data of a particular region (the United States). That means our sample is biased and is not representative of the population.
Another important statistical concept to understand is distribution. When the population is infinitely large, it’s not feasible to validate any hypothesis by calculating the mean value or test parameters on the entire population. In such cases, we assume a population is some type of a distribution.
While there are many forms of distribution, the most common are binomial, Poisson and discrete.
You must determine the distribution type to calculate the critical value and decide on the best test to validate any hypothesis.
Now that we’re clear on population, sample and distribution, let’s learn about different kinds of tests and the distribution types for which they are used.
We use a t-test to compare the mean of two given samples. A t-test assumes a normal distribution of the sample. When we don’t know the population parameters (mean and standard deviation), we use t-test.
There are multiple variations of the t-test:
The statistic for this hypothesis testing is called t-statistic, the score for which we calculate as:
t=(x1 — x2) / (σ / √n1 + σ / √n2) , where
x1 = mean of sample 1
x2 = mean of sample 2
n1 = sample size 1
n2 = sample size 2
Note: This article focuses on normally distributed data. You can use z-tests and t-tests for data which is non-normally distributed as well if the sample size is greater than 20, however there are other preferable methods to use in such a situation.
We use the chi-square test to compare categorical variables.
A small chi-square value means that data fits.
A large chi-square value means that data doesn’t fit.
The hypothesis we’re testing is:
The statistic used to measure significance, in this case, is called chi-square statistic. The formula we use to calculate the statistic is:
Χ2 = Σ [ (Or,c — Er,c)2 / Er,c ] where
Or,c = observed frequency count at level r of Variable A and level c of Variable B
Er,c = expected frequency count at level r of Variable A and level c of Variable B
We use a t-test to compare the mean of two given samples but we use the chi-square test to compare categorical variables.
In a z-test, we assume the sample is normally distributed, similarly to a t-test. A z-score is calculated with population parameters such as population mean and population standard deviation. We use this test to validate a hypothesis that states the sample belongs to the same population.
The statistic used for this hypothesis testing is called z-statistic, the score for which we calculate as:
z = (x — μ) / (σ / √n) , where
μ = population mean
σ / √n = population standard deviation
If the test statistic is lower than the critical value, accept the hypothesis.
We use analysis of variance (ANOVA) to compare three or more samples with a single test.
The hypothesis we’re testing with ANOVA is:
The statistics used to measure the significance in this case are F-statistics. We calculate the F-value using the formula:
F= ((SSE1 — SSE2)/m)/ SSE2/n-k , where
SSE = residual sum of squares
m = number of restrictions
k = number of independent variables
There are multiple tools available such as SPSS, R packages, Excel etc. to carry out ANOVA on a given sample.
In all of the tests introduced in this article, we’re comparing a statistic with a critical value to accept or reject a hypothesis. However, the statistic and the way to calculate it differ depending on the type of variable, the number of samples you’re analyzing and whether or not we know the population parameters. We can thus choose a suitable statistical test and null hypothesis. This principle is instrumental to understanding these basic statistical concepts.
A t-test compares the means of two given samples, and is best for continuous, numerical data. A chi-square test compares the differences between two categorical variables.
Chi-square tests are used to compare two categorical variables. This is to determine whether observed results align with expected results and if the null hypothesis of the two variables being independent is true.
A chi-square test may be applied to determine if a sample accurately matches the population or to compare two variables in a contingency table to check if the data fits.
T-tests are used to compare the means of two different samples or groups, where the samples are assumed to have a normal distribution. This test is most suitable for continuous data and when the mean and standard deviation of a population are unknown.
A t-test may be applied to compare means for two groups, compare the means from the same group at different times or to test the mean of a single group against a known mean.
A chi-square test is used to compare discrete, categorical data. Both the chi-square goodness of fit test and chi-square test of independence require categorical variables to be applied.
An analysis of variance (ANOVA) test is used to compare the differences in results between three or more unrelated groups. A one-way ANOVA test compares the difference in the means of three or more samples/groups. A multivariate analysis of variance (MANOVA) test is used to test the effect of one or more independent variables on two or more dependent variables.
A z-test is used to test a hypothesis when population variance is known, such as comparing the means of two populations or comparing a sample mean and population mean. Z-tests must use normally distributed and independent data, and they are best performed with a sample size greater than 30 data points.
