Compare clinical significance and statistical significance

Discussion Questions

 

Data analysis is key for discovering credible findings from implementing nursing studies. Discussion and conclusions can be made about the meaning of the findings from the data analysis.

· Share what you learned about descriptive analysis (statistics), inferential analysis (statistics), and qualitative analysis of data; include something that you learned that was interesting to you and your thoughts on why data analysis is necessary for discovering credible findings for nursing.

· Compare clinical significance and statistical significance; include which one is more meaningful to you when considering application of findings to nursing practice.

Compare clinical significance and statistical significance; include which one is more meaningful to you when considering application of findings to nursing practice

Introduction

When considering applications of findings from scientific research, it is often helpful to consider both statistical and clinical significance. Clinical relevance of findings may be important, but not always possible to establish with certainty. Clinical implications of findings depend on context and situation, rather than the statistics involved in their collection or interpretation.

Compare clinical significance and statistical significance

  • Statistical significance is concerned with whether a result has been found by chance. For example, if you flip a coin 10 times and get 3 heads in a row, this would not be considered to be statistically significant because there’s still an equal chance of getting another head or tail on each flip. In contrast, clinical significance is concerned with whether an outcome should be considered important for clinical practice. For example, if your client has chest pain and no history of heart disease but their blood pressure is elevated after eating pizza (a typical reaction), this may indicate that they have high cholesterol levels which require treatment with medication such as statins (cholesterol-lowering drugs).

Clinical significance is concerned with the question of whether an outcome should be considered important for clinical practice.

Clinical significance is concerned with the question of whether an outcome should be considered important for clinical practice. Clinical significance is not the same as statistical significance. It is more closely related to practical measures, such as how often a result occurs in practice and how much it contributes to your understanding of a disease process or patient response.

It may be difficult to determine exactly what factors contribute most strongly to clinical significance, but there are some common themes that have been identified in research:

  • How often does this outcome occur in other studies?
  • What are the possible causes or mechanisms behind this finding?

Statistical significance is concerned with whether a result has been found by chance.

The term ‘statistical significance’ is not the same as clinical significance. Statistical significance refers to whether or not a result has been found by chance alone, whereas clinical significance is concerned with whether the result may be meaningful in relation to your patient’s illness or condition.

In order for you to understand and use this information effectively in your practice, it is important that you understand what statistical significance means and how it can be applied.

When considering applications of findings from scientific research, it is often helpful to consider both statistical and clinical significance.

When considering applications of findings from scientific research, it is often helpful to consider both statistical and clinical significance. Although all three are important, they are not equivalent in meaning or importance.

Clinical relevance refers to the practical application of research findings for nursing practice. For example, if a study shows that women with low socioeconomic status have poorer outcomes than those who have higher SES (socioeconomic status), this may be useful for informing your decision about whether or not you should encourage students from disadvantaged backgrounds into your program. However, since this finding only has clinical relevance when applied within the context of your teaching and learning efforts—in other words: You need to figure out how best to work with these women in order for them to improve their health outcomes—it is not necessarily as valuable as a statistical result showing that despite having similar demographic characteristics (e.g., socioeconomic status), one group had lower mortality rates than another due simply because they were less likely participants in clinical trials sponsored by pharmaceutical companies).

Clinical relevance of findings may be important, but not always possible to establish with certainty.

Clinical relevance is a more subjective concept than statistical significance, so it should be considered less frequently in determining whether findings are clinically significant. However, there are times when the results of a study may not be statistically significant but still have clinical relevance. For example, if a test shows that one group of patients has an increased risk of developing pneumonia compared to another group (and these results can be replicated), then this result would likely be considered clinically relevant even though it did not reach statistical significance levels.

Because clinical significance is often more difficult to establish with certainty, many researchers prefer to use their own judgment when considering how important their findings should be for nursing practice. They might look at how similar previous studies were in terms of outcome measures and design characteristics; they might consider whether any research evidence exists supporting or contradicting their hypotheses; and finally they may weigh all available information against each other before making decisions about what conclusions should guide future research efforts based on those findings

Clinical implications of findings depend on context and situation, rather than the statistics involved in their collection or interpretation.

Clinical implications of findings depend on context and situation, rather than the statistics involved in their collection or interpretation. Clinical significance is concerned with whether an outcome should be considered important for clinical practice. Statistical significance is concerned with whether a result has been found by chance.

Both types of significance are useful in their own right.

Clinical significance and statistical significance are two very different types of significance. Clinical significance is useful for clinicians, who are concerned with the practical implications of research findings. It can be used to help determine how well a treatment works or how effective a new intervention will be in treating a condition. For example, if you find that one medication has fewer side effects than another medication for your patients with diabetes, this could be considered clinical evidence because it indicates which treatment might be better suited for them based on their individual needs and preferences.

Statistical significance refers to whether an observed result is likely due to chance rather than being caused by something else (for example, if 10% of results were significant at p <0.05). This type of inference relies on having enough data points so that we can estimate our unknown population parameters; however this process may require significant time investment (time-consuming) as well as large amounts of resources such as personnel resources needed during data collection phase or computer equipment required during processing phase (elevation/depression).

Conclusion

The term “significance” has two meanings, and both are used in scientific research. The first is a statistical concept that determines whether or not results were found by chance. The second meaning is related to the clinical importance of findings from scientific studies. Both types of significance are useful in their own right, but it can be helpful to be familiar with both so that you can make an informed decision about which one matters most when considering applications of findings from scientific studies.

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