What is a “null hypothesis” and why is it sometimes considered a better starting point for experimental work than an “alternate hypothesis”?

A "null hypothesis" is a statistical hypothesis that assumes that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error. It is often symbolized as H0.

The null hypothesis is sometimes considered a better starting point for experimental work than an "alternate hypothesis" for several reasons:

1. Objectivity: The null hypothesis assumes that any kind of difference or importance you see in your data is due to chance. This means you start your experiment without expecting to find something specific, which helps to prevent bias.

2. Simplicity: The null hypothesis is generally simpler than the alternative hypothesis, as it involves assumptions of equality or no effect. This makes it easier to disprove, and science often prefers simpler explanations.

3. Falsifiability: The null hypothesis is designed to be falsifiable using statistical tests. If the data does not support the null hypothesis, it is rejected in favor of the alternative hypothesis. This is a fundamental part of the scientific method.

4. Burden of Proof: The null hypothesis places the burden of proof on the researcher. It is assumed to be true until evidence indicates otherwise. This encourages rigorous experimental design and analysis.

The "alternate hypothesis" or "research hypothesis", on the other hand, is the hypothesis used in hypothesis testing that is contrary to the null hypothesis. It is usually taken to be that the observations are the result of a real effect (with some amount of chance variation superposed).