# How to interpret contingency table 3 x2 with Yates correction?

I ran a contingency test and obtained the following results

             A         B          C
Low          8         15        26
High         5         25        15

Pearson's Chi-squared test
X-squared = 5.9842, df = 2, p-value = 0.05018


How do I interpret these values? From the looks of it, it says that "Low" prefer "C" and that is significantly different from "High" that prefer "C". But how do I formally note that this is true?

• Technically the p-value is not <0.05, but anyway... suppose the result is significant, from the Chi square test you can only conclude that the expected frequencies do not match the observed frequencies in one or more categories. It does not tell you which categories, you need a different model / test for that. – user2974951 Jul 23 '19 at 6:08
• What do you think is an apprporaite test for that – Biotechgeek Jul 23 '19 at 15:17
• If you want to get comparisons then you can use a Generalized (Poisson) Linear Model. – user2974951 Jul 24 '19 at 11:44