How to interpret the p-value for chi-square-test I am fairly new to statistics and programming. I hope my question is neither stupid nor trivial.
I have 8 categories, and every category should have the same count of 156.125. My empirical observation is: [200, 156, 133, 178, 148, 130, 150, 154]. I calculated $\chi^2$ = 23.88390712570056.
Now, I want to calculate the critical value and here I am starting to struggle.

$H_0$ would be my hypothesis that the distribution is uniform. Since $p=0.95$ is around 14, I assume I have to reject $H_0$. But the higher my $p$-values go, the higher the critical value goes. This means that around $p=0.9999$ my calculated $\chi^2$ would be below the critical value and thus, I won't have to reject $H_0$ with a much higher $p$-value. This confuses me because I would have assumed, to have $H_0$ not rejected, the $p$-value has to go down.
What am I doing wrong? Thank you so much in advance.

Comment: What do you mean by "[E]very category should have the same count of 156.125"? Does that mean you're doing a chi-squared test of homogeneity?
In R:
x = c(200, 156, 133, 178, 148, 130, 150, 154)

 chisq.test(x)

        Chi-squared test 
        for given probabilities
data:  x
X-squared = 23.884, df = 7, p-value = 0.001194

The small P-value indicates indicates categories are not
all equally likely.
Note: When specific probabilities are not supplied, the R procedure chicsq.test assumed equal probabilities are intended.
There are $8$ categories, so $7 = 8-1= DF.$
Critical value (5%) for chi-squared statistic is $c = 14.07,$
meaning that $H_0$ is rejected at 5% level if
chi-square statistic exceeds $c.$
qchisq(.95, 7)
[1] 14.06714

In the figure below the vertical dotted line is at
$c = 14.067,$ so that 5% of the area under the density curve lies to the right of $c.$

