Essay Example on Developing Critical Thinking for Evidence-Based Practice

Introduction

Critical thinking is the ability to analyze facts carefully to come up with a judgment. It evolves around attempts to bring conclusions. Evidence-based practice is the conscientious application of present evidence encompassed with patient values and clinical expertise in the provision of quality health care. Critical thinking requires nurses to reason before they decide on cases affecting patients purposely to provide quality health care. Critical thinking for evidence-based practice in overall help medical practitioners to develop quality diagnosis and medication possible to aid in the achievement of desired outcomes without harming the patient. The paper focuses on the effects of inclusion and exclusion criteria in a study, the importance of power analysis to the researcher, effects of adequate sample size in concerning different areas during the study, and any cosmic question that may arise during the study.

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How Inclusion and Exclusion Criteria Affect the Strength of Evidence in A Sample

When it comes to the reading of research reports, the number one issue of consideration is whether the researcher was able to identify the characteristics of the population that would form the basis of the inclusion (eligibility) or delimitations method used in the selection of the samples (LoBiondo-Woods & Haber, 2018). That is, whether they used people, objects or events. Inclusion and exclusion are definite contributors to the research because they give insight into the criteria that the researcher will follow in deciding about the samples that will not be in the study and samples that will be included in the study (LoBiondo-Woods & Haber, 2018). The decision of the researcher will depend entirely on the criteria that he or she has set aside depending on the type of research to be conducted. The factors to be considered by the researcher might include the following; age, medical diagnosis, gender, social-economic status, and many others. The primary purpose of this criteria is to control cases of biases that might occur during the study and weaken the strength of the pieces of evidence that are caused by a sampling plan linked to the design of the study (LoBiondo-Woods & Haber, 2018). To maintain the validity of exclusion or inclusion, there should be evidence that supports the contamination effect on the dependent variable. For instance, samples might be excluded in the study because it had a history of metastatic cancer or had already received their chemotherapy, unlike the required samples that had not undergone any treatment (LoBiondo-Woods & Haber, 2018). The main benefit of using inclusion or exclusion criteria are that it contributes to the accuracy of the research findings. When carefully done, the resultant evidence of the study will automatically be precise and robust.

The Importance of Power Analysis to A Researcher

Estimating the sample size required is possible statistically with the use of power analysis when following the needed procedures. When calculating sample sizes, a power analysis is the essential part to consider. Failure to use this procedure, the study can be regarded as null because it will be detrimental (LoBiondo-Woods & Haber, 2018). The size of the sample should be appropriate to the study whatsoever because it should not be too large or too small for validity reasons. When the sample is too small or too large, it may cause a ripple and endless spiral effects that may lead to failure of the study (LoBiondo-Woods & Haber, 2018). Therefore, the appropriate size of the sample is beneficial to the study because the results would be accurate. The researcher must put into consideration all the means possible to aid the success of the study.

Effects of Adequate Sample Size in Different Scenarios

Adequate sample size affects subject mortality in a way that, participants may drop out on the process of the research and affect the results either positively or negatively. In this case, the mortality rate can be solved before the onset of the study by stopping the subjects who find the exercise a threat to their health from participating. Representativeness of the sample can easily be affected by the size of the sample because it is a reflection of the whole population of the example. The validity of the results might be hindered by how the relationship between the two is handled (Sheldon, 2020). The ability of the researcher to detect a treatment effect depends entirely on the size of the sample. When the number of samples is few, the error will also be significant. Similarly, when the size of the sample is more, the individual study will be easy, and the confidence level will be very high (Sheldon, 2020). The ability of the researcher to generalize his study to the population depends on the size of the sample used to conduct the research. The magnificent examples are not easy to separate, but when the results of the study can be applied to the whole group of samples, then the investigation will eventually be termed to have good generalizability.

Cosmic Question

In which ways can a sample size be detrimental to a study?

In conclusion, we have learned the effects of inclusion and exclusion criteria in our study as well as the critical role played by power analysis to the researcher. The impact of sample sizes to various scenarios have indicated different results in every situation, and we have also paused a question to be answered by the class.

References

LoBiondo-Wood, G., Haber, J., & Titler, M. G. (2018). Evidence-Based Practice for Nursing and Healthcare Quality Improvement-E-Book. Elsevier Health Sciences.

Sheldon, T. A. (2000). Estimating treatment effects: real or the result of chance? Evidence-Based Nursing, 3(2), 36-39. http://dx.doi.org/10.1136/ebn.3.2.36