Statistically significant and clinically significant evidence
HCA 699 Topic 6 DQ 2
What is the difference between statistically significant evidence and clinically significant evidence? How would each of these findings be used to advance an evidenced-based project?
ADDITIONAL DETAILS
Statistically significant and clinically significant evidence
Introduction
A randomized controlled trial is considered a gold standard for determining whether or not a drug or treatment is effective. An RCT can have both positive and negative effects, but it’s important to understand how to interpret the results of an RCT before making a decision about whether or not to use a particular treatment. In this post, we’ll discuss why some large studies may be statistically significant but not clinically significant and vice versa. You’ll also learn about the difference between statistical significance and clinical significance in RCTs:
Statistical significance
Statistical significance is a measure of how likely it is that the results are due to chance. The more statistically significant an effect, the less likely it is that our results were due to random variation.
Statistical significance does not mean that your study has shown a clinically significant difference between groups; it just means there’s less likelihood of finding differences due to chance alone. Clinical significance refers to whether or not you can say with confidence that one group has been harmed by treatment compared with another group (e.g., whether one group had fewer complications than another).
Clinical significance
Clinical significance is a subjective term. It means that the results are important to the patient, doctor and health care system.
The determination of clinical significance in research studies is based on whether or not there are statistically significant differences between groups of patients or between individual patients with respect to their symptom severity (i.e., how severe their symptoms were).
Significance and risk reduction
The smaller the risk reduction, the more likely it is to be statistically significant. For example, if you have a study that involves 200 people and finds that taking antibiotics reduces your chances of getting sick by 10%, then this is considered significant because it’s less than 1%. However, if you have a study with 1 million participants and find that taking antibiotics reduces your chances of getting sick by 5%, then this isn’t considered statistically significant because even though there are only 500 participants left in your sample size after removing all those who got sick anyway (and therefore should not have been included), their results still show a lower risk than what was seen in the first group at all – which means they weren’t as effective at reducing illness as they could be!
It also helps to think about how many people need to show statistical significance when comparing two different treatments or interventions; for example: If one treatment has 90% effectiveness while another has 50%; then ideally both should produce statistically significant results so we get an idea how good each one really is. As long as both show similar levels of success among patients who receive them during clinical trials (see below), then we know these approaches probably work similarly well too!
A large study may be statistically significant but not clinically significant.
A large study may be statistically significant but not clinically significant.
Statistical significance is a measure of the probability that the results are due to chance, and it refers to whether or not there was any difference between two groups at all. Clinical significance on the other hand is a measure of how much difference there was between groups in terms of size or strength; it’s important because if you want to know whether your treatment works better than another one (or if you’re trying to find out why someone has symptoms), then having an idea about what kind of effect size will help you decide whether or not something should be tested further by researchers who do real research instead of just doing experiments based on money (like doctors).
A smaller trial may be clinically significant but not statistically significant.
A smaller trial may be clinically significant but not statistically significant.
A large trial may be statistically significant but not clinically significant.
A small trial may be both clinically and statistically significant, or just one or the other, depending on how you look at it.
Key points to remember when evaluating the results of a randomized controlled trial.
When you are evaluating the results of a randomized controlled trial, it is important to remember that statistical significance is not the same as clinical significance. Statistical significance refers to whether or not there is a significant difference between two groups of people or if there are changes over time in one group compared with another. Clinical significance refers to whether or not these results have any practical implications for your life and your health care decisions.
For example: A study might show that taking statins reduces heart attacks by 10% (statistical significance). However, this doesn’t mean that taking statins will reduce your risk of dying from heart disease by 10%. It could even be worse—it might just mean that taking statins will give you more pain from side effects than no treatment at all!
To determine if something has any real-world relevance we need more than just statistics; we need practical information about how likely our results would apply outside of the laboratory setting into real life situations like those experienced by patients who take medication every day or undergo surgery every couple years instead of once every decade due largely because they don’t know what works best until after they’ve already started experiencing symptoms themselves so they can’t predict beforehand what would have happened had everything gone according plan during actual treatment sessions–and thus cannot choose wisely based on past experience alone.”
Conclusion
It’s important to remember that these are just some of the many factors that need to be considered when interpreting the results of a randomized controlled trial. In addition, there is always the possibility for bias in any science or medical procedure, so we must also evaluate the study design and conduct by others who have had similar experiences with other treatments or lifestyle changes before engaging in our own self-experimentation. We hope this blog post has given you some insight into how clinical significance and statistical significance can be evaluated and understood by scientists and doctors alike!
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