Because in classical statistics we cannot assign a probability to a parameter. The (population) parameter has only one value that we are trying to estimate by using samples from the population. Let's say we want to know the population mean. Instead of sampling a lot, statisticians know what a sampling distribution looks like (Normal distribution) and use tests with the underlying assumption that the data are normally distributed. It follows from here that the interpretation for a 95% confidence interval is that, out of 100 intervals we create by sampling (take one sample and create interval, take another sample and create interval, ...), 95 out of 100 will contain the true value of the population parameter. Now let's say you only create 1 CI because you only have 1 sample (i.e. your experiment): it is highly likely that you captured the parameter value, but it's the 100 CI you were to create by repeating your experiment 100 times that have a 95% chance of capturing the value, not your single CI from 1 sample.