Hypothesis Testing in Healthcare Research

The vast majority of current research is explicitly correlated with the scholars’ consideration of an assumption that could be either proved or rejected by the empirical evidence. Such an approach to health care development and innovation is known as hypothesis testing and stands for the establishment of correlation between dependent and independent variables that result in a tangible statement about the population (Ambrose, 2018). Although hypothesis testing is widely used within other scholarly paradigms such as sociology and psychology, its application to the sphere of medicine is crucial for the development of theoretical frameworks that contribute to one’s treatment.

There are two most common examples of how hypothesis testing is used in research. The first example concerns the outline of a null hypothesis or a hypothesis that secures no correlation between the variables (Chiang et al., 2015). For example, when conducting a study concerning one’s predisposition for cardiac diseases and socio-financial background, the null hypothesis will state that there is no relationship between the two. Later, empirical data will be gathered to prove or reject the assumption. The second example concerns the introduction of an alternative hypothesis that appeals to the existence of a relationship between the variables (Chiang et al., 2015). Moreover, the relationship that exists within the study sample serves as a reflection of the patterns of development within the population. Thus, when considering the same example, an alternative hypothesis will state the existence of a causal link between the variables and their application to the overall situation.

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In order to define the extent to which a hypothesis may be accepted and considered seriously within the academic community, the researchers have come up with a quantitative indicator of a “probability of a result at least as extreme as the sample result if the null hypothesis were true” (Chiang, “The misunderstood p-value” section). This phenomenon is known as a p-value, and its established norm is generally accepted to be 0.05 (5%). If the result indicates a probability of less than 5%, the null hypothesis is rejected.

The phenomenon of hypothesis testing is crucial for the interactions with patients and innovations in medical practice. Thus, when interacting with patients on a daily basis, nurses are automatically exposed to a great number of empirical data that eventually leads to the genesis of some regulations and assumptions. The justification of these assumptions provides patients with more personalized care with a higher probability of positive patient outcomes.

References

Ambrose, J. (2018). Clinical inquiry and hypothesis testing. In Applied statistics for health care (Grand Canyon University). Web.

Chiang, I-C. A., Jhangiani, R. S., & Price, P. C. (2015). Research methods in psychology (2nd Canadian ed.). Web.

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