# NUR 705 Discussion 5.1: Statistical Inference and Clinical Significance

## NUR 705 Discussion 5.1: Statistical Inference and Clinical Significance

Statistical inference is the process of making interpretations about certain populations based on statistical values, while clinical significance is much more subjective, and may be used to determine the measure of something (Anaesth, 2021). A statistical inference implies that something is happening, while the clinical significance is a measure of how much that thing is changing, measured by an expert in the field (Dahlberg et al., 2021).

Statistical data can be easily swayed depending on sample size and measure variability, so clinical significance is incredibly important to determine if the statistical information is clinically important (Anaesth, 2021). Clinical trials that are performed in large numbers are reliant on statistical information to determine credible results (Anaesth, 2021). Mostly, the two rely on each other to make relevant and reliable research study results.

Sharma H. (2021). Statistical significance or clinical significance? A researcher’s dilemma for appropriate interpretation of research results. Saudi journal of anaesthesia15(4), 431–434. https://doi.org/10.4103/sja.sja_158_21 (Links to an external site.)

Dahlberg, S.E., Korn, E. L., Le-Rademacher, J., Mandrekar, S. J. (2020). Clinical versus statistical significance in studies of thoracic malignancies. Journal of Thoracic Malignancies. 15(9), 1406-1408. https://www.jto.org/article/S1556-0864(20)30477-9/fulltext

Hello Airion

I agree with you that statistical inference tells us that something is happening. It also tells us that what is happening is not by chance and to what degree it is not by chance considering the sample size of the data, bias and the levels of measurement variability. while we can tend to trust this information to be a base of further enquiry, it does not represent or provide clinical information for patient care (Kim et al. (2022) and Sharma, (2021)). Clinical significance of a study on the other hand tells us how effective the result of the study is in terms of treating patients to return to improved health and meaningful quality of life Sharma, 2021). Improved health outcome or effective change in treatment modality is the goal of determining clinical significance of a study (Schober et al., 2018).

Kim, M., Mallory, C., & Valerio, T. (2022). Statistics for evidence-based practice in nursing (3rd ed.). Jones & Bartlett Learning.

Schober, P., Bossers, S. M., & Schwarte, L. A. (2018). Statistical significance versus clinical importance of observed effect sizes. Anesthesia & Analgesia,126(3), 1068–1072. https://doi.org/10.1213/ane.0000000000002798 (Links to an external site.)

Sharma, H. (2021). Statistical significance or clinical significance? a researcher’s dilemma for appropriate interpretation of research results. Saudi Journal of Anaesthesia15(4), 431. https://doi.org/10.4103/sja.sja_158_21

Yes, this is all true. It is also true that the the two work together, as i clinical trials, or evidence-based practice could be an example of statistical inference and clinical significance working together. In the example of evidence-based practice using both statistical inference and clinical significance would be using the statistics of how to treat a certain disease/illness, based on which interventions had the best outcomes. Statistics really is about numbers, the numbers are the only thing that matters. Unfortunately, numbers aren’t always incredibly significant in healthcare, or the real world. This is why the clinical significance portion is so important. Clinical significance sets the numbers from the statistical portion into what is actually important and what is not. Statistical inference takes all of the information gathered into consideration, while clinical significance filters this information so that only the useful information is left.

Kim, M., Mallory, C., & Valerio, T. (2022). Statistics for evidence-based practice in nursing (3rd ed.). Jones & Bartlett Learning.

Hello Airion

Great discussion post. From your analysis, I agree with you that statistical data can be easily swayed depending on sample size and measure variability, so clinical significance is incredibly important to determine if the statistical information is clinically important (Matsouaka & Coles, 2020). When reviewing research studies, it is essential to evaluate both statistical inference and clinical significance. Statistical inference is important in order to understand whether the findings of a study are due to chance or if they are meaningful. However, even if a study has strong statistical evidence, this does not necessarily mean that the findings are clinically significant (Yadlowsky et al., 2021). Clinical significance refers to the real-world impact of a treatment or intervention. In other words, even if a treatment is shown to be statistically effective, it may not have much of an impact on patients’ lives (Ramos-Vera & Ogundokun, 2021). For this reason, it is essential to consider both statistical inference and clinical significance when reviewing research studies.

References

Yadlowsky, S., Yun, T., McLean, C. Y., & D’Amour, A. (2021). Sloe: A faster method for statistical inference in high-dimensional logistic regression. Advances in Neural Information Processing Systems34, 29517-29528. https://proceedings.neurips.cc/paper/2021/hash/f6c2a0c4b566bc99d596e58638e342b0-Abstract.html (Links to an external site.)

Matsouaka, R. A., & Coles, A. (2020). Robust statistical inference for the matched net benefit and the matched win ratio using prioritized composite endpoints. arXiv preprint arXiv:2011.10720.
https://doi.org/10.48550/arXiv.2011.10720 (Links to an external site.)

Ramos-Vera, C. A., & Ogundokun, R. O. (2021). The use of the Bayes factor for statistical inference. https://rua.ua.es/dspace/bitstream/10045/113872/6/JHSE_16-2_22.pdf

There’s a significant difference between statistical inference and clinical significance. Statistical inference is like a very well educated guess. A conclusion is made about a population based off of a sample group (Stat Analytica, 2020). Clinical significance shows the effect of how a specific treatment on a sample group. For example if a clinical trial is testing a drug and it shows to be not effective in the sample group, that doesn’t necessarily mean one could statistically inference the drug not being effective for the rest of the population. The opposite is true as well. Statistical inference compared to clinical significance shows potential vs actual.

The reason why it’s important to evaluate clinical significance and statistical inference together is because those conducting the study will know the results aren’t just by chance.  It’s also imperative that the results of the study show either a positive or negative outcome to determine if the treatment was effective or not. Clinical significance and statistical inference could be viewed together as both being positive, both being negative, or one positive with the other negative (Statistical Significance vs. Clinical Significance, 2017). Just because a result shows something is statistically significant does not mean it is clinically significant. For example if a clinical trial were testing a blood pressure medication that showed to reduce the blood pressure of patients and it were to be statistically significant shown by the P value, but clinically speaking there’s only a reduction of blood pressure by 5, than it’s not clinically significant. This shows the importance of why both clinical significance and statistical inference needs to be looked at together in order to view the picture of the study as a whole, and draw conclusions based off the data.

Stat Analytica. (2020, April 8). Statistics Inference : Why, When And How We Use it? – StatAnalytica. https://statanalytica.com/blog/statistics-inference/?amp

Statistical significance vs. clinical significance. (2017, March 23). Students 4 Best Evidence. https://s4be.cochrane.org/blog/2017/03/23/statistical-significance-vs-clinical-significance/

Hello Stephanie,

In reference to your example, the statistical significance will inform the probability that the change in blood pressure is due to the intervention of use of a drug. However, the reduction of blood pressure by 5 points maybe clinically significant depending on if we are assessing the drug as a stand alone new drug or we are comparing it with another treatment of choice which will guide decision making regarding including this drug in the patient’s treatment regimen. This is why it is important to have both statistical significance and clinical significant results of a study in decision making.

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Hello Stephanie

Great discussion post. I concur with you that there’s a significant difference between statistical inference and clinical significance. Although the terms “statistical inference” and “clinical significance” are often used interchangeably, they actually have different meanings. Statistical inference refers to the process of using data to make conclusions about a population (Yadlowsky et al., 2021). For example, you might use statistical inference to determine how many people in a population have a certain characteristic (e.g., are obese). Clinical significance, on the other hand, is concerned with the practical implications of findings. For example, if you find that a particular treatment is effective in reducing obesity in a population, then that treatment would be considered clinically significant. It is essential to reviewing research studies because statistical inference lets you know how likely it is that the results of a study occurred by chance, while clinical significance tells you how important the results of a study are (Mukhopadhyay, 2020). Statistical inference is the process of calculating how likely it is that the results of a study occurred by chance. This calculation is done by looking at the size of the difference between the groups being studied and determining how likely it is that such a difference could have arisen by chance alone (Harrison et al., 2020). This number (the p-value) is expressed as a percentage and tells you how likely it is that the observed difference occurred due to chance.

References

Harrison, A. J., McErlain-Naylor, S. A., Bradshaw, E. J., Dai, B., Nunome, H., Hughes, G. T., … & Fong, D. T. (2020). Recommendations for statistical analysis involving null hypothesis significance testing. Sports biomechanics19(5), 561-568. https://doi.org/10.1080/14763141.2020.1782555 (Links to an external site.)

Mukhopadhyay, N. (2020). Probability and statistical inference. CRC Press.

Yadlowsky, S., Yun, T., McLean, C. Y., & D’Amour, A. (2021). Sloe: A faster method for statistical inference in high-dimensional logistic regression. Advances in Neural Information Processing Systems34, 29517-29528. https://proceedings.neurips.cc/paper/2021/hash/f6c2a0c4b566bc99d596e58638e342b0-Abstract.html (Links to an external site.)

Stephanie,

My initial interpretation of statistical inference and clinical significance importance were a little different than yours. I stated that statistical inference was numerical based on population size versus clinical significance importance which looked at the impact of the intervention. You discussed the two topics and potential versus actual results. That said, I do agree with you. While statistical data looks at research from a numerical standpoint, it only takes into consideration the population size, making the results an inference. I really like your examples they help clarify this week’s topics. Now that we are aware of the topics, I think it would be beneficial to share a research article that has the components of statistical inference and clinical significance importance. Understanding the concepts now helps us as students pick these out of articles and understand the values. Have you ever worked with statistical inference and clinical significance importance outside of class? I know we all have different nursing backgrounds and I wonder do you find these topics relevant to your work now? You discussed the P-value in your post. Perhaps you can expand more on this topic? The P-value is seen in almost every article I read for class, and it was very commonly seen in my undergraduate research articles. Your post is very well written and I think having more elaboration on this topic will bring this week’s content altogether.

Stephanie,

Yes, there is a complete difference between statistical inference and clinical significance. When I began looking at the differences between the two, I thought of it a little differently, as I thought of statistical inference as being the facts. I looked at the statistical inference as the cold hard facts, or the numbers, because numbers don’t lie (Sharma, 2021). It is interesting to me for you to see this in another way, as you call it an educated guess. I probably never would have gathered that view on my own, but I can grasp what you are saying by that. Statistics are purely facts that have been gathered on the topic, but the numbers, or facts, is all that is there. In “real life” things don’t always just numbers or facts. Clinical significance is a factor that determines which parts of the information gathered is important (Sharma, 2021). You are very right, though, statistical inference has a high probability of favoring a decision based off of a study in one small population, and stating that the drug or treatment is ineffective, or negative in some way. When a study only uses statistical inference to draw information, there can be many variables (Sharma, 2021). For instance, a study being done on a medication to treat depression. The population is made up of mostly postmenopausal women, the medication doesn’t show a significant improvement in this population’s depression symptoms, and the statistical inference study will automatically decide that this is a negative outcome, or mark the drug as unsuccessful. The weakness of this study, is the quick judgement of the drug because it may not be extremely effective in postmenopausal women, but could work great for young adult men. The statistical inference- based study is facts based. The facts are that, the medication was unsuccessful in treating the individuals in the study population, so the drug is never attempted with any other population. In this case, clinical significance could be used to determine if the drug may be better targeted to a different population, clinicians can better determine these results based on experience.

Sharma, H. (2021). Statistical significance or clinical significance? a researcher’s dilemma for appropriate interpretation of research results. Saudi Journal of Anaesthesia15(4), 431. https://doi.org/10.4103/sja.sja_158_21

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