I am testing the agreement of two sets of data with each other, albit testing for the goodness-of-fit. However, both experimental data and model data has actually a lot of zeros in it so **chi-squared test fails**. I am a bit at lost now, what would be the correct fitness test for these kinds of scenario?

Note: Data are measurements of physical quantities rather than counts, so there will be a lot of fractional values.

Example:

```
Expected Actual
0        0
0        0
1        0.9

```

And there are about thousands of remaining data set not shown.

If we use chi-squared

$$ X^2=\sum\limits\_j \dfrac{(O\_j-E\_j)^2}{E\_j} $$

We will get undefined, since one of the elements is over zero.