Is Chi Square test statistic too large to draw conclusions? I collected some data regarding purchase behaviour (purchasing a black or white car), before and after updating the background color of an e-commerce website.
I would like to know if the change was significant, so I applied the Chi Square statistic formula, and found a test statistic for the full table of 76.6 (it is the sum of the 4 cells = 14+13+25+25, red cell below).
However, when I look at the Chi square distribution table, with 1 degree of freedom, the test statistic 76.6 does not even appear on the table and I get stuck.
I read online about the Chi Square Goodness of Fit test.. by computing the 76.6, did I actually already made the fit test, without being aware of it? Shall I conclude that 76.6 is a value too large, and Chi Square is not the right model to fit my data?
Please find below also the data with observed, expected and test statistic. Thanks!

 A: Your question is quite unclear. But if you are doing
a chi-squared test of independence on the table TAB
below, results from chisq.test are as below.
The very large chi-squared statistic $76.64$ and correspondingly
tiny P-value (nearly $0)$ are shown. [Because of
the large counts, I did not use Yates' continuity correction).]
    TAB = rbind(c(2402, 1585), c(2810, 1229))
    TAB
          [,1] [,2]
     [1,] 2402 1585
     [2,] 2810 1229

    chisq.test(TAB, cor=FALSE)

            Pearson's Chi-squared test

    data:  TAB
    X-squared = 76.643, df = 1, p-value < 2.2e-16

A chi-squared statistic larger than $3.8415$ would
have led to rejection at the 5% level (also $6.6349$
at the 1% level; $10.8276$ at the 0.1% level).
    qchisq(c(.95,.99,.999), 1)
    [1]  3.841459  6.634897 10.827566

So your chi-squared statistic $76.634$ is too large to be found in a printed table, but leads to rejection
at a very small level of significance.
A: @BruceET's has already answered but since you are asking about drawing conclusions, I would like to add a couple of points.
With many counts - in the order of thousands - many real-world problems would give a very small p-value. However, I wouldn't confuse a small p-value for an effect that must necessarily be important. In your case the CW update seems to affect white cars more than black cars, the percent changes being
$$
\rm Change_{white} = log(2810) - log(1229) = 83\% 
\\
Change_{black} = log(2402) - log(1585) = 42\%
$$
However, this difference may or may not be practically relevant. Also, if you had 100 times fewer counts (like: 28, 12, 24, 12) you would have a non-significant p-value even if the effect size stays the same.
The other consideration is that the $\chi^2$ test assumes counts coming from purely random draws like balls from a bag. In practice, there is often additional  variation between different categories so you may get a large differences even if nothing interesting is actually happening.
