Interpretting a chi-sq correctly I apologize for what may seem a very simple question. I can't seem to find an answer to how I can interpret chi-sq results other than rejecting the null hypothesis.
Pearson's Chi-squared test with Yates' continuity correction
X-squared = 5.2538, df = 1, p-value = 0.0219

I understand that I am able to reject the null hypothesis given the p-value. Can I also interpret the results saying that Procedure B is more likely to lead to a malignant result given the observed results are greater than expected?
 A: For interpreting the test itself you should stick to interpreting the p-value, however nobody stops you from saying that Procedure B led to more malignant results than expected under the independence null hypotheses. You may even get away with the interpretation that you suggested, however this is not directly what the test says.
The test is defined by null and alternative hypothesis, test statistic and result (p-value). None of these says anything in particular about what results are "likely" with Procedure B. My point is that on top of that you can explain and interpret how your data led to this result. Nothing wrong with that, but don't mix the term "significant" in there; "likely" is kind of borderline and one can discuss for long whether this is justified. I wouldn't use it.
A: In addition to what is highlighted be @Lewian, the end of the statistical procedure (here a chi-squared test) and what you do next may seem unrelated, but there is an interaction that needs to be considered.
But first a mention of the statistical test results. This is predicated on the idealized nature of the test, and requires the assumptions to be met. A non-zero number of low p-values probably occur because of treatments being chosen non-randomly, that is the choice is in some way influenced by characteristics of a given individual.
But lets suppose this is not an issue with the data above, and the assumptions are met. Then if you do a subsequent hypothesis, we know that (because of the low p-value) there is a difference between the expected frequencies and the observed frequencies in the two categories. Although it is natural to want to interpret this difference, in some cases (which we do not know which) in the absence of outside information (such as known biological mechanisms) we can only say so much. A simple example is if the low p-value was just through chance (that is if the treatments each have the same effect, for example if neither treatment has an effect) then further information obtained from the data is still related to the chi-squared test result that it is unknown of whether the result was due to chance. A low p-value tells us something about the null hypothesis is wrong, it does not tell us of it is real, due to chance, or because of a violation of the test assumptions. Uncertainty remains even though low p-values are often interpreted as strong evidence, from which strong causes can be interpreted.
The Wikipedia page on HARKing classifies this situation as THARKing, transparently hypothesizing after the results are known, rather than the secretive, undisclosed, HARKing that was first proposed by Kerr (1998). Of the 12 potential costs of HARKing given by Kerr, one is translating Type I errors into hard-to-eradicate theory.
