Testing hypothesis may be the most used and most misunderstood statistics tool. When we do even a simple fitting, and want to evaluate the fitting result, we have to use the hypothesis testing. One common quantity used is the reduced chi-squared.

A hypothesis testing means given an observation and hypothesis, Is the hypothesis NOT true? right, hypothesis test never tell us the trueness  of the hypothesis, but the wrongness of it. The core of the test is “Can we reject the null hypothesis?

There are one-tailed and two-tailed testing, as a result, the p-value has different meanings.

https://en.wikipedia.org/wiki/One-_and_two-tailed_tests

https://en.wikipedia.org/wiki/P-value

The p-value is the probability that the model agree with the observation. when the p-value too small, smaller than the confident level, the null hypothesis Rejected. But if the p-value is very large, in a 1-tailed test, we cannot say the null hypothesis is true, but we can say the null hypothesis CANNOT be rejected.

In 2-tailed test, there are two p-values, corresponding to each tail.

https://en.wikipedia.org/wiki/Confidence_interval

https://en.wikipedia.org/wiki/Type_I_and_type_II_errors