As a businessperson, it is hard to share

It is both good and bad to have to include results if a businessman's hypothesis testing does not reach a certain significance level. Both the consequences has been explained below -
Good impact -
-These tests determine whether the sample evidence is strong enough to suggest that an effect exists in an entire population.
-It allows us to draw conclusions about an entire population based on a representative sample.
-These data support the theory that an effect exists at the population level.
-The results were statistically significant in that the similar measurements of intelligence between races are not merely sample error.
Bad impact -
-Too many mistakes are made in the misplaced emphasis on significance tests.
-Just because a bad trading strategy typically produces an unprofitable backtest, it in no way guarantees that a trading strategy isn't bad when it generates a profitable backtest.
-These hypothesis are sometimes bounds to have an error reflecting the hypothesis does not reach a certain significance level.
Besides hypothesis testing following are the alternative ways to validate the research findings in a business environment -
Z- test - A z-test is a statistical test used to determine whether two population means are different when the variances are known and the sample size is large.
${\displaystyle z=\frac{x-\mu }{\frac{\sigma }{\sqrt{n}}}}$
x=sample mean,
u=population mean,
${\displaystyle \frac{\sigma }{\sqrt{n}}}$ = population standard deviation.
-T- test - In t-test the mean of the two given samples are compared. A t-test is used when the population parameters (mean and standard deviation) are not known.
-Chi square test - chi-square test is used to compare two categorical variables. Calculating the Chi-Square statistic value and comparing it against a critical value from the Chi-Square distribution allows to assess whether the observed frequency are significantly different from the expected frequency.
${\displaystyle x^2=\sum \frac{{(o-e)}^2}{e}}$
o =observed,
e=expected.