Hello Professor and class,

In statistical hypothesis testing, the -level represents the threshold for rejecting the null hypothesis and is often set at 0.05, indicating a 5% risk of making a Type I error (false positive) (Frost, 2023). However, selecting an appropriate -level should consider the context and potential consequences of an incorrect conclusion.

Lower -levels: A lower -level, such as 0.01, is appropriate in situations where a false positive could have serious consequences. In a medical laboratory setting, for example, consider testing for a highly infectious pathogen, such as HIV in donated blood. A false positive could lead to unnecessary patient anxiety, additional costly confirmatory tests, or even the inappropriate disposal of safe blood units. Therefore, using a more stringent -level reduces the likelihood of mistakenly identifying a healthy sample as infected, increasing confidence in the test result (McClave & Sincich, 2021).

Higher -levels: Conversely, a higher -level, such as 0.10 or 0.20, may be acceptable in exploratory research or preliminary testing where missing a potential effect (Type II error) is more concerning than a false positive. For example, in a lab conducting preliminary assays for a new biomarker, being overly cautious might prevent detecting an early signal of clinical importance. Accepting a slightly higher risk of false positives allows for the identification of potential leads that can later be confirmed with more rigorous testing (Frost, 2023).

In my daily work as a Medical Laboratory Scientist, understanding the implications of different -levels helps guide decision-making regarding test reliability and patient safety. By applying this knowledge, I can balance the risk of errors against the need for timely and accurate diagnostic results, enhancing both clinical outcomes and laboratory efficiency.

-Patricia Asturias-Escobar

References:

Frost, J. (2023). Hypothesis testing and significance levels. Statistics by Jim. https://statisticsbyjim.com/hypothesis-testing/significance-levels-alpha/

McClave, J. T., & Sincich, T. (2021). Statistics (13th ed.). Pearson.

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