I have N 2x2 observed contingency tables. Each one was sampled from the same distribution. The problem is the column labels are obfuscated so that I do not know the true label name of each column, and in some contingency table the first column refers to the same label that in a different contingency table is the second column.

If I had one contingency table, I would not care that the column labels are obfuscated in order to check independence between the variables, I can still calculate the chi-squared statistic and run the chi-squared test.

But since I have N contingency tables with the columns maybe permuted and no way to discern the column labels, I cannot build one big contingency table.

Would it make sense here to calculate the chi-squared statistic for each table separately then take the sum of all of these and calculate a p-value on that?

It seems to make sense but I want to make sure I am not missing something obvious


1 Answer 1


In fact that is a common technique in meta-analysis of significance values. The original approach by Fisher transforms the $p$-values to a $\chi^2_2$ because there is an easy formula for that but Lancaster suggested doing it for any number of degrees of freedom. So, yes, just add them up and you have a $\chi^2_k$ where you had $k$ tables.

  • $\begingroup$ ah great! Thanks. Do you have a source explaining this technique? $\endgroup$ Commented Jun 30, 2021 at 6:07
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    $\begingroup$ I think it is in Lancaster, H O in Biometrika 1949 36 370-382 but it is quite an intensive paper and I do not have the time right now to read it in detail. $\endgroup$
    – mdewey
    Commented Jun 30, 2021 at 12:27
  • $\begingroup$ Thanks! I'll try to find it. $\endgroup$ Commented Jul 1, 2021 at 6:44

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