Null findings are often not as useful as you might think.

A point null hypothesis for a continuous variable is literally always false. Especially for the softer science, I've even seen studies mocked for having too many data points, since it's known that with enough data null hypotheses are false.

The story is better if your null hypothesis is an interval, but then you're really just obliquely using the interval to bound something you could be measuring more directly anyway.

What I'd like to see is moving away from null hypothesis testing altogether and focusing on measuring things. For example, focusing on measuring effect sizes, or the probability that a hypothesis is true.