Z test and Chi-square are two different statistical hypotheses testing. Both tests give an alternate point of view to null value hypotheses.
Key Takeaways
- Statistical tests: Z-test is a hypothesis test using the standard normal distribution to compare a sample statistic to a population parameter. In contrast, the chi-square test is non-parametric, comparing observed frequencies to expected frequencies under the null hypothesis.
- Data type: Z-test is used for continuous data, while the chi-square test is used for categorical data.
- Applications: Z-test is employed for testing the mean or proportion of a single population, while the chi-square test is utilized for independence, the goodness of fit, or homogeneity tests.
Z-Test vs Chi-Square
The Z-test is used when the sample size is large and the population standard deviation is known, used to test hypotheses about the mean of a normal population. The Chi-square test is used when the sample size is small, thus used to test hypotheses about the distribution of a categorical variable.
Z-test is used for dealing with problems relating to large samples (n>30). It is easier to use when the standard deviation is available.
The Chi-square test was used for testing relationships between categorical values. The null hypotheses of the Chi-square say that two categorical variables in the population should be independent.
Comparison Table
| Parameter of Comparison | Z-Test | Chi-square |
|---|---|---|
| Statistic used | The statistics used for the alternate hypothesis testing is called Z-statistic. | The statistics used for null hypothesis testing is called the Chi-square statistic. |
| Null and Alternate values | Invalid: The sample mean is the same as the population mean. | Null: Both Variables C and D are independent. |
| Alternatively, it can be said that the results of the sample mean and population mean should be different. | Alternative: Both variable A and variable B are not independent. | |
| Conditions | The standard deviation should be known. The sample size should be large enough, or else z-test may not perform well. The test statistics should follow a normal distribution. | There should be a minimum of five observations at each variable level. The test can be done only if there are categorical values. The sampling method should be simple and random. |
| Formula | z = (x-μ)/(σ / √n) Where, x = sample mean. μ = population mean. σ / √n = standard deviation. | Χ2 = Σ(O − E)2/E Where, O = each Observed (actual) value E = each Expected value |
| Uses | Determines if the results of two means obtained from two populations are different when the variance and data are large | It uses categorical data in comparing two or more groups where the values are mentioned. |
What is Z-Test?
A Z-test is nothing but a type of hypothesis test. The samples are distributed while conducting the test. It is used only when there is a standard deviation, and the sample data should always be extensive (n>30).
In other words, it validates hypotheses drawn by the sample to the same population.
Conditions required to perform a Z-test:
- The sample data should be greater than 30.
- The data points should be independent of each other; that is, there should be no similarities or overlapping.
How to run a Z-test?
- First, the null (H0) and the alternative hypothesis (HA) must be stated.
- Then, choose the alpha level.
I am advised that Z-test should analyse the null hypothesis when the data is on a large scale, and the standard deviation is known.
What is Chi-Square?
The Chi-Square test is best defined as a statistical hypothesis test. This test is used either for comparing a group with a value or multiple groups with categorical data.
The advantages of this test are the robustness of the given data. It can only be used when two categorical variables are related to some population.
The Chi-square test is a goodness-fit statistic because it measures how well the observation data fits the distributed data. It can only happen when the two given variables are independent.
Main Differences Between Z-Test and Chi-Square
- In Z-test, the samples are evenly distributed, whereas, in Chi-square, it should be simple and randomly selected from the given population.
- Both tests used different methods but were used for giving alternate hypotheses to the null value hypotheses.
- https://www3.nd.edu/~kyuan/papers/nest-chisq-z.pdf
- https://www.sciencedirect.com/science/article/pii/S0167947313003204
Last Updated : 11 June, 2023
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The article could have delved more into actual applications and examples of when to use each test.
I agree, practical examples would have been beneficial.
The article provides a clear breakdown of Z-test and Chi-square. It’s a great introduction for those unfamiliar with these concepts.
Certainly a good starting point for someone new to statistics.
The post reads like a helpful tool for understanding the differences between Z-test and Chi-square, great work!
An insightful read, it’s good to have these concepts clearly delineated.
I couldn’t agree more, an informative piece breaking down these statistical tests.
The post shows a clear distinction between the Z test and the Chi-square test, giving readers a very educational and informative look at the subject matter.
I agree, these tests are confused and it’s refreshing to see them both explained clearly.
I was hoping for a more detailed explanation on when to use each test. I feel like that part was slightly skimmed over.
I don’t believe that level of detail was necessary in this article.
I agree, a deeper look into the real world applications would have been beneficial.
A comprehensive article explaining the Z-test and Chi-square, well done!
Very well written, a good reference for these important statistical tests.
The article is quite informative but could benefit from a more engaging tone. Statistics can be dry and difficult for some readers.
I think the straightforward nature of the article is its strength.
I agree, a more engaging voice would be beneficial for the less data-inclined readers.
The article provides a thorough comparison between the Z-test and Chi-square, making it easier for readers to understand the nuances of each test.
Exactly, it’s so important to know when to use which test, and this article helps with that.
Agreed, there’s no more room for confusion after reading this article.
I appreciate the comparisons, it’s such an important part of understanding these statistical methods.
Absolutely, comparisons clarify the differences and help in knowing when to use each test.
The use of comparisons and illustrations in the article really help solidify the understanding of Z-tests and Chi-squares.
Absolutely, visual aids and clear examples can greatly enhance the learning experience.