Solving a Chi-Squared Test Divide by Zero Error

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A divide by zero error in a chi-squared test occurs when the expected value is zero, making the test statistic calculation impossible. The chi-squared test is unreliable with expected frequencies below 5, and especially with zero. In such cases, a Fisher's Exact Probability Test is recommended for 2x2 contingency tables with small expected frequencies, typically less than 10. If neither test is suitable for the data, alternative methods may need to be explored. It is crucial to ensure that expected frequencies are adequate for accurate statistical analysis.
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What should I do when the expected value for a chi squared test is zero, so when I try to calculate the test statistic, i get a divide by zero?
 
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The chi-squared test isn't very accurate when expected frequencies are less than 5 (and definitely not if they are 0). In some cases, you can instead use a Fisher's Exact Probability Test (if you have a 2x2 contingency table and small expected frequencies...I think less than 10). I don't have the formula for that handy though. If that doesn't work for your data, I'm not sure what other alternatives there are.
 

Thread 'Hypothesis testing: Defining H0, HA hypotheses so that ( H_A)_A' makes sense'

The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...
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