11.5 Conclusions

The interpretation of the results based on whether the p-value is less than your chosen significance level (α), will make you conclude if there is enough evidence to support the evidence or not.

In addition the two types of errors represents the percentage of the population who were correctly/incorrectly identified answering the research question.

The specific values of Type I and Type II errors will vary based on the simulation, but by adjusting the parameters in the code, you can explore different scenarios and see how changes in test sensitivity (1 - β) and specificity (1 - Type I error rate) affect these error rates.

Furthermore, two distinct approaches to hypothesis testing exist: Neyman’s approach, which emphasizes controlling Type I errors and facilitating binary decisions while optimizing test power, and Fisher’s approach, which prioritizes assessing the strength of evidence against the null hypothesis, often employing p-values and confidence intervals, without rigidly controlling Type I error rates. Researchers select the most suitable approach based on their research objectives and priorities, often incorporating both perspectives when conducting and interpreting hypothesis tests.