Obtaining unexpectedly large p-value (chi square) i have no much experience coding nor with statistics so sorry and thank you in advance.
I'm trying to find out if two variables (living in urban or rural areas) and using medicine A or B (due to accessibility) are related. I have a data frame with many other variables and thousands of cases on which i'm doing the analysis. This is the grouped data
$data
           Outcome (medicine)

Predictor 1    2    Total

Urban     1412 1792  3204
Middle    885  1167  2052
Rural     485  662   1147
Total     2782 3621  6403 

I've tried using both chisq.test, and oddsratio.wald, obtaining with both a p.value of around 0.54, thus i can't reject the Null hypothesis and should assume that both may not be related at all, nevertheless if we look at the percentages we can see that there is a trend, in which the more rural the area, the less percentage use medicine 1.
          % use of Total
Urban     44.07% 
Middle    43.13%  
Rural     42.28%

So shouldn't i be obtaining a very low (<0.05) p value? am i doing something wrong?
I've used:
chiurba<-chisq.test(dfbi$TIPOANTICONCEP,dfbi$DEGURBA,simulate.p.value = TRUE)

and
ORurba<-oddsratio.wald(dfbi$DEGURBA,dfbi$TIPOANTICONCEP)

Thank you in advance

Statistical significance is a measure for how probable the magnitude of a certain observed effect is when there would have been, in reality, no effect.
The motivation for this is that we use observations to measure some population, but our measurements have variations and do not exactly represent the true values of the population.
If certain samples are not accurate or very representative, due to statistical variations in the sampling or measurements, then we may still measure a certain effect even if there is not a true effect.
We call a measurement statistically significant if, assuming that there is no effect, the magnitude of an observed effect is not likely to occur due to the statistical variations.

So you measure some differences 44.07%, 43.13%, 42.28%.
But, for the given sample sizes there will likely be such variations and differences in a sample even if the true percentages in the population are equal.
