Introduction Chi-square and ANOVA Tests
In this blog, discuss two different techniques such as Chi-square and ANOVA Tests. Both are hypothesis testing mainly theoretical.
The Chi-Square test is a statistical procedure used by researchers to find out differences between categorical variables in the same population. Read more about ANOVA Test (Analysis of Variance)
The P-value is to decide whether we should accept or reject the Null Hypothesis. If the p-value lower than the pre-determined significance value (i.e.alpha or threshold value) then we reject the null hypothesis. The alpha should always be set before an experiment to avoid bias.
For Example, consider a population data to be in normal distribution so while selecting alpha for that distribution we select approx it 0.05 (i.e. accepting 95% of our distribution). This means that if our p-value is less than 0.05 and reject the null hypothesis.
Chi-square statistical method commonly used for testing a relationship between categorical variables. In statistics, there are two types of variables: numerical (countable) variables and non-numerical (categorical) variables. The null hypothesis of the Chi-square test is that no relationship exists on the categorical variables in the population and they are the independent variables. The chi-square test can be used to determine whether observed frequencies are significantly different from expected frequencies.
O = observed score
E = Excepted score
A low value for chi-square means there is a high correlation between your two sets of data.
The hypothesis being tested for chi-square is
Null: Variable A and Variable B are independent.
Alternate: Variable A and Variable B are not independent.
Types of Chi-square
There are two types of chi-square tests. But Both of use chi-square statistics and distribution for different purposes.
chi-square goodness of fit test
It determines if a sample data matches a population.
chi-square test for independence
Compares two variables in a contingency table to and check they are related or not. In a more general sense, it tests to see whether distributions of categorical variables differ from each other.
A very small chi-square test statistic means that your observed data fits your expected data. In other words, there is a relationship.
A very large chi-square test statistic means that the data does not fit very well. In other words, there isn’t a relationship. Read more about Beginner’s Guide to Statistics in Machine Learning and SciPy.