Outcome : Frequency

A : 2

B : 3

C : 0

D : 5

E : 2

Now, I have a model that predicts the frequency. However, the predictions are non-negative real numbers. For example:

Outcome : Predicted frequency

A : 1.75

B : 2.77

C : 0.11

D : 3.82

E : 3.55

I attempted to use the Pearson's Chi-square goodness of fit test to evaluate . However, since the values of frequency are below 5, I read that this test is not the best choice (= reliable). So, I attempted to use Fisher's exact test. The limitation of Fisher's exact test is that the data has to be non-negative

*integers*.

**My question**: Is there a way to evaluate the p-value between the observed and predicted sets of data where the predicted data are not integers, but non-negative real numbers. The values of frequency are often lesser than 5.