It's been many years since I've been in Academia, so I may be wide of the mark, but my interpretation of what the referee is saying – loosely speaking – is that your experimental data is "too easy" on your hypothesis. While your experiments tend to suggest the hypothesis might be true ("results tried to confirm [...] the model"), they do not (in the opinion of the referee) sufficiently "stress" the hypothesis by including "difficult" data.
Taking an analogy with software engineering, it is common practice to perform unit testing on any new block of code. While it is important to include tests that represent "normal" input data (that "[try] to confirm [...] the model"), it is also important to include "edge cases" and "difficult values" that are essentially chosen to try and "break" the new code ("attempt to reject the model").
For a simplistic example, consider a function
add( param1, param2 ) designed to add two numbers together. A simple, if slightly naive, set of tests might check that
add(10,10)==20. Passing these tests gives some evidence that the function is working correctly, but they don't really stress the code. Suppose that instead of writing
result = param1 + param2 in the body of the function, you had
result = param1 + param1 (either through "finger trouble" when first creating it, or a later search-and-replace that changed more than intended). Those tests will still pass, but they won't detect that the function is merely doubling the first parameter.
In conclusion, I believe the referee is saying that while the experimental data you've included does tend to support your hypothesis, you've not chosen a sufficiently wide range of experimental data, and that you cannot claim that the hypothesis appears to be true even in the face of experiments deliberately designed to disprove it.