if the null hypothesis is rejected in hypothesis testing, no conclusions can be drawn from the test. the sample size has been too small. the alternative hypothesis is true. the data must have been accumulated incorrectly.

The relationship between important factors of interest and any apparent connection, according to a competing theory, is most certainly not accidental. The opposite of a null hypothesis is an alternative hypothesis. If we are able to rule out the null hypothesis, it signifies that we have enough data to support the alternative hypothesis. In one test, we cannot rule out both possibilities.

If one is disproven, it indicates that there is evidence to support the other. The opposing view is that a study's potential conclusion will materialize. It claims there is no coincidence in the relationship between important components of interest and the observed connection. As a result, the alternative hypothesis is true if the null hypothesis is disproved during hypothesis testing.