Chi square formula is used to compare two or more statistical data sets. The chi square formula is used in data that consist of variables distributed across various categories, and helps us to know whether that distribution is different from what one would expect by chance.
Example: You research two groups of women and put them in categories of student, employed or self-employed.
The numbers collected are different, but you now want to know
Is that just a random occurence? Or
Is there any correlation?
What is the Chi Square Formula?
The chi-squared test checks the difference between the observed value and expected value. The chi-square formula is:
χ2 = ∑(Oi – Ei)2/Ei
where Oi = observed value (actual value) and Ei = expected value.
The Chi-Square test gives a P-value to help you know the correlation if any!
A hypothesis is in consideration, that a given condition or statement might be true, which we can test later. For example
A very small Chi-Square test statistic indicates that the collected data matches the expected data extremely well.
A very large Chi-Square test statistic indicates that the data does not match very well. If the chi-square value is large, the null hypothesis is rejected.
Chi-Square test statistic is called P-value. The P-value is short for probability value. It defines the probability of getting a result that is either the same or more extreme than the other actual observations. The P-value represents the probability of occurrence of the given event. The P-value is used as an alternative to the rejection point to provide the least significance for which the null hypothesis would be rejected. The smaller the P-value, the stronger is the evidence in favor of the alternative hypothesis given observed frequency and expected frequency.
P-value ≤ 0.05
It indicates the null hypothesis is very unlikely.
P-value > 0.05
It indicates the null hypothesis is very likely.
Accepted or it “fails to reject”.
P-value > 0.05
The P-value is near the cut-off. It is considered as marginal
The hypothesis needs more attention.
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