Null hypotheses are often idealized distributions that are mathematically convenient and are often over-simplifications of the distributions we'd expect if there were truly no effect (because the expected distributions are either intractable to work with, or irregular and unknown).
So for example, suppose you want to detect if there's unusual patterns in website traffic -- a bot attack or unexpected popularity spike. You look at page views per hour over several days, with the null hypothesis that page views are normally distributed, with constant mean and variance over time.
You run a test, and unsurprisingly, you get a really low p-value, because web traffic has natural fluctuations, it's heavier during the day, it might be heavier on weekends, etc.
The test isn't wrong -- it's telling you that this data is definitely not normally distributed with constant mean and variance. But it's also not meaningful because it's not actually answering the question you're asking.
So for example, suppose you want to detect if there's unusual patterns in website traffic -- a bot attack or unexpected popularity spike. You look at page views per hour over several days, with the null hypothesis that page views are normally distributed, with constant mean and variance over time.
You run a test, and unsurprisingly, you get a really low p-value, because web traffic has natural fluctuations, it's heavier during the day, it might be heavier on weekends, etc.
The test isn't wrong -- it's telling you that this data is definitely not normally distributed with constant mean and variance. But it's also not meaningful because it's not actually answering the question you're asking.