# Top Assumptions for Running a Chi-Square Test

## Introduction

The chi-square test is basically a test that is related to statistical investigation which is used in comparing the observed results with the expected results in the SPSS data analysis. The primary purpose of the square analysis is to determine that if there is a difference between the observed data and the expected data or not due to the chance and if this difference is due to the relationship in between all the variables that are applied or not. Other than the chi-square test, there are two more tests named – T test and Z test. All of these three are somehow related with each other but there are differences among each of them.

There are several advantages of the test that make it very popular among researchers. The students need to know about all of them in detail so that they can also avail them while using it. The first is that it can test all sorts of categorical data that fall into distinct groups such as size, color, and many more. It is seen Assumptions of chi square test example are likely to be very decent. The next one is that it does not require any kind of data in place to follow a particular pattern which makes it a very feasible test. The test also can do the calculation in a very simple manner which makes it the first preference of the researchers. It is seen that the test is capable of handling large samples which is very good for the ones who are doing big studies. The test also allows us to do work with several categories. It is said that the chi-square test example is a good way to know all the advantages of the test.

It is also very essential to know the disadvantages of the test so that they can be mitigated by the researcher while using it for evaluating the data. There are a few disadvantages to using the test such as the test can only be used for the categorical data which states that it cannot be used for the numerical data. The other is that it is a very sensitive test in terms of the size of the sample. The test needs a large amount of sample size to get accurate results which restricts the test for several studies. The test is also not capable of telling the impact caused by one variable on the other one. All the Assumptions of chi square test spss are studied by the researchers so that they do not make any mistakes.

## The need for a Chi-Square test:

Several reasons make this test very beneficial for the students and researchers are there are many benefits of using it. Few studies are extremely large and the collection for it needs a test that can evaluate all the findings accurately without making any error. It is a fact that assumptions of chi-square test are known to everyone so they keep necessary points into consideration while using it. Many researchers claim that this test has made their work very easy as now they can evaluate large data. It is assumed that the Chi-square test of independence SPSS is a very good point of the test. The test got very popular due to its capacity to evaluate the data.

## Assumptions of Chi-Square Test

The difference between the chi-square test and the t-test is that the t-test is simply a null hypothesis applied in the statistical hypothesis test most often regarding the two means – first is that it tests that the hypothesis test of the two means is equal or not and the second is that the difference in between them is zero or not. Whereas, the z-test is generally used only for the purpose when there is given a standard of deviation along with the data, which can be larger than the size of 30. Apart from these two tests, the chi-square test is also a type of null hypothesis in the chi-square statistic hypothesis about the relationship among the two variables but the difference is that it is used only at that time when the two categorical variables are independent apart from each other and also belongs to the same population at the same time. The Chi-square test spss, a hypothesis testing method involves two of the most common Chi-square tests that check if the observed frequencies involved in one or more categories are matching with the expected frequencies or not. The “Chi” is a Greek word whose symbol is “χ”. There are lots of assumptions of the chi-square test spss, but few of the top assumptions can be included as –

There are only 2 variables where both of them are usually measured as different categories the nominal level but the data can be the ordinal data. The intervals or the data ratio which is collapsed into the ordinal categories can be used too with the Chi-square to have no rule regarding the cell numbers limitation.

The data filled in the cells must be in frequencies or in any counts of the cases rather than just in the percentages or in some of the other transformations of the sample data collected.

Each of the subjects of this test can contribute the data to the one and only in one cell of the χ2. As an example, if the same subjects are being tested over and over again in the given time then such the comparisons come up of the same subjects at the Time 1, Time 2, Time 3, etc. and whatever more time are given which the χ2 might not be used with.

The expected cell value should be either 5 or more than 5 among at least 80% of the total cells with that no cell should be expected to be less than one. This assumption can only be fulfilled if the sample size is equal to at least the number of total cells then multiplied by 5. This assumption essentially can be specified with the number of sample size cases to use the χ2 for any of the number of cells in that χ2

Here the study groups needs to be independent so that different tests can be used differently when the two groups are related.

## Conclusion

The Chi-square test of independence SPSS is one of the most often applied statistics for testing the case hypotheses when there are nominal variables that often occurs in the clinical research work. The “Chi” is a Greek word whose symbol is “χ”.The chi-square test is also a type of null hypothesis in the chi-square statistic hypothesis about the relationship among the two variables but the difference is that it is used only at that time when the two categorical variables are independent apart from each other and also belong to the same population at the same time.