I've been reading a bit about the confidence intervals on Wikipedia. The section on misunderstandings says:
A 95% confidence level does not mean that for a given realized interval there is a 95% probability that the population parameter lies within the interval (i.e., a 95% probability that the interval covers the population parameter). According to the strict frequentist interpretation, once an interval is calculated, this interval either covers the parameter value or it does not; it is no longer a matter of probability. The 95% probability relates to the reliability of the estimation procedure, not to a specific calculated interval.
I read through an article making the same point. It concludes by saying:
The nature of confidence intervals is that they can encompass the true value with some chance. In our example, a confident interval tends to encompass the true value in 90% of trials. But that does not mean that for some specific interval, there is a 90% chance of finding the true value within the interval.
But I really don't understand the point of this argument. What is the difference between saying "there is a 90% probability that the true value is within our interval" and "there is a 90% probability that our interval included the true value"? Is it just a philosophical issue or can it really lead to incorrect conclusions?