Knowing that sleep quantity and quality affects cognitive performance across many domains, why aren't pre-test sleep measures or intra-test measures of arousal a standard part of all cognitive test paradigms?
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Measures of arousal surely do play a role in subject performance on a wide variety of cognitive tasks. Generally, scientists can safely ignore this factor as it is assumed to introduce random noise between participants. From a hypothesis testing perspective, scientists are much more worried about factors that introduce systematic bias, or factors that skew performance in one condition but not another.
As an example, consider a psychologist that is testing whether some intervention has an effect on performance. If we assume that arousal levels throughout the sample are randomly distributed, it should not affect our ability to detect an effect of our independent variable. In fact, there are potentially infinitely other factors that may affect performance on a cognitive task. To name a few: prior experience, mood, fatigue, cognitive load, intelligence, or attention. Each one of these factors in turn is the result of numerous other incidental variables (for instance, my mood may be affected by the weather, or how long it's been since I've eaten).
It would be infeasible to test for all of these factors, but again, if we assume that each measure is randomly distributed throughout a sample, it won't make a big difference-- in fact, it's somewhat statistically convenient:
Many psychological measures are normally distributed, which allow us to run statistical tests that assume normality (e.g., t-tests!) The reason this is so is because what we are measuring is actually the sum of many independent and identically distributed (i.i.d.) variables-- such as arousal, experience, attention, etc. The Central Limit Theorem tells us that the sum of all of these factors lead to a normally distributed measure.
It is still important to know when and how sleep measures may affect performance. In knowing that information, scientists can watch out for situations in which arousal may cause systematic bias between conditions, a potential confound. But in general, it often just doesn't really matter for the tests we are conducting.