Think about it: You’re presenting a journal club. Someone asks, “Was the primary end point statistically significant?” You answer yes and move on. But then another preceptor follows with, “Okay … but would this change what you do tomorrow?” Suddenly, the conversation becomes much more complicated.

As pharmacists, we’re taught early on to look for statistical significance when evaluating literature. We check the P value, glance at the confidence interval, and decide whether the study met any of its end points. But statistical significance is only 1 piece of the puzzle. To determine whether a study should influence patient care, we also need to consider its clinical significance.

Statistical significance helps answer 1 question: Is the observed difference likely real, or could it simply be due to chance? Two tools commonly help us answer this. First, if a confidence interval does not cross the point of no difference (eg, 1 for ratio data, or 0 for difference data), the result is considered statistically significant. Second, if the P value is less than the predetermined α level (typically 0.05), the result is also considered statistically significant. These measures tell us about the reliability of the observed difference but not necessarily its importance.

Clinical significance asks a different question: If the difference is real, does it actually matter? This is where pharmacists can have a tremendous impact on evidence-based decision-making. A statistically significant result may not justify changing practice if the benefit is small, the medication is prohibitively expensive, the treatment carries substantial risks, or the outcome itself has limited importance to patients. Similarly, patient preferences, feasibility, and the overall balance of benefits and harms all influence whether a statistically significant finding translates to real-world clinical practice.

One way to think about clinical significance is to consider whether the benefit of an intervention is worth the cost, risk, or burden required to achieve it. For example, some people joke that you can treat a wart by placing duct tape over it. Although the evidence behind this approach may be debated, the concept is useful: for a low-risk and inexpensive intervention, some patients may be willing to try a treatment with modest potential benefit before pursuing a more invasive option. However, this decision-making process changes when the intervention is expensive, inconvenient, or associated with substantial risks. The same principle applies when evaluating medications and other health care interventions—the potential benefit must be weighed against the overall impact on the patient.

The distinction between statistical and clinical significance may become clearer with a few additional examples. Imagine a very large randomized trial that finds a statistically significant 1% absolute reduction in cardiovascular events with a new medication. With a large sample size, even a very small difference may produce an impressive P value. But is a 1% reduction enough to justify adopting a new standard of care? The answer depends on far more than statistical significance.

Now consider the opposite scenario. A medication demonstrates a statistically significant 15% reduction in cardiovascular events but is also associated with a 30% increase in major safety events. Despite the trial’s meeting its primary end point, clinicians would need to carefully weigh the risks against the benefits before recommending widespread use of the medication. Statistical significance tells us the finding is unlikely due to chance; clinical significance helps us decide whether it is worth acting on.

In many studies, statistical and clinical significance go hand in hand. However, neither guarantees the other. A statistically significant result is not automatically clinically meaningful, and a clinically meaningful effect may fail to reach statistical significance if a study is underpowered. As pharmacists, our responsibility is to evaluate both before considering how they fit together.

The next time you’re reading a clinical trial, don’t stop once you find the P value. Ask yourself 2 questions: Is the observed difference likely real? and If it is, will it make a meaningful difference for my patients? Statistical significance may tell us whether a finding deserves our attention, but clinical significance helps determine whether it deserves a place in practice. As pharmacists, we are uniquely positioned to bridge this gap—to look beyond the numbers and help translate evidence into meaningful decisions for our patients. This is the other half of the story.