Why the p value matters for statistical significance in social work research

Statistical significance is one of those buzzwords that sounds technical but lands right in our everyday sense-making. In social work research, a finding that's “significant” often feels like a green light—proof that what we’re seeing isn’t just a fluke. But what exactly signals significance? The quick answer is: a p-value. Let me explain what that little number does, and why it matters for understanding results that could shape real-world decisions.

What is the p-value, in plain English?

Think of a p-value as a measuring stick for chance. Imagine you’ve got a hypothesis: a new outreach program helps families reduce stress. The null hypothesis says, “No, the program didn’t change anything.” The p-value asks: if there really is no effect (if the null is true), how likely is it that we’d see results as extreme as the ones we observed just by luck?

That might sound abstract, but the idea is simple: a small p-value means the observed effect is unlikely to be a random fluke, given the assumption of no effect. In other words, the smaller the p-value, the stronger the evidence against the idea that “no difference” is all there is. It’s not a guarantee, but it’s a signal that our data nudges us toward believing there’s something real going on.

A quick note on thresholds

You’ll hear numbers like 0.05, 0.01, or 0.001 tossed around. These are commonly used thresholds for declaring something statistically significant. If the p-value is below the threshold, researchers often say the result is significant at that level. So:

• p < 0.05 is the most familiar cutoff; many studies flag anything below five in a hundred as noteworthy.

• p < 0.01 is a stricter standard, suggesting stronger evidence.

• p < 0.001 is even more stringent, indicating very strong evidence.

These thresholds aren’t universal rules carved in stone; they’re conventions that help researchers communicate confidence levels. They also depend on the field and the stakes of the decisions being informed.

Measuring significance vs. describing data

Now, let’s pause and separate two different jobs in numbers.

• Descriptive statistics (mean, standard deviation, etc.) tell us about the data we actually collected. The mean gives the center of our sample; the standard deviation tells us how spread out the data are; the correlation coefficient shows whether two variables tend to move together and in which direction.

• Inferential statistics (like the p-value) tell us something about the population from which our sample came. They address questions like: could this observed pattern be due to random variation, or does it point to a real effect?

So yes, the mean can tell you that a group’s average distress score is 7 out of 10, and the standard deviation might show there’s a lot of variability. But neither number on its own says whether that distress pattern is likely to show up again if we looked at a different group. The p-value is the bridge to that broader question.

A practical lens for social work

In social work research, significance isn’t magic; it’s a cue. It helps us prioritize which findings deserve attention, more follow-up, or cautious implementation. But significance alone doesn’t tell the full story. A result can be statistically significant but practically small. Imagine a new counseling technique lowers average symptom scores by a tiny amount. With a large enough sample, that tiny improvement might still look statistically significant, even if it won’t move the needle in real life.

That’s where effect size and confidence intervals come in. The effect size asks: how big is the change? The confidence interval gives a range where we’re reasonably sure the true effect lies. If that range is tiny, the practical impact might be modest even with a small p-value. If the range covers meaningful improvements for families or communities, even a modest p-value could carry weight for policy or program decisions.

Common misinterpretations to watch for

A lot of beginners, and some seasoned researchers too, mix up significance with meaning. Here are a few practical cautions:

• Significance is not “proof.” A small p-value doesn’t guarantee the finding will hold up in every context or population. It means the observed data are unlikely under the null hypothesis, given the study design and sample.

• Bigger samples can turn tiny effects into significant results. If you study a thousand families, you’ll detect smaller differences than you would with a hundred. That doesn’t automatically mean the difference matters in the real world.

• The opposite isn’t true either. A non-significant p-value doesn’t prove there’s no effect; it might mean the study wasn’t powerful enough to detect it.

• Multiple testing inflates false positives. If you run many tests, some will appear significant by chance. Proper adjustments or pre-specified plans help keep interpretations honest.

• The p-value depends on the model and assumptions. If the data don’t meet those assumptions, the p-value can be misleading.

A simple social-work-friendly example

Let’s ground this with a concrete example. Suppose a community organization tests a home-visit program aimed at reducing caregiver strain. They randomize 200 families to receive the visits or to a control group. After three months, caregiver stress scores dip a bit more in the intervention group. The analysis yields p = 0.03 for the group difference.

• What does this mean? At the standard 5% threshold, the team would say the difference is statistically significant. If the null hypothesis is true (no real effect of the home visits), getting this or a more extreme result would happen only about 3 times in 100 by chance.

• What doesn’t it tell you? It doesn’t tell you how big the benefit is in a practical sense. It doesn’t tell you whether the program will work in another city or with families from a different background. It doesn’t tell you if there were biases in who participated.

• What should you look at next? Check the effect size to gauge practical impact. Look at the confidence interval to see the precision of the estimate. Consider the program’s cost, feasibility, and ethical implications before deciding on broader adoption.

How to report p-values in plain language

If you’re sharing findings with nonstatisticians—policymakers, program staff, or community partners—clear reporting matters. You can say:

• “The intervention reduced caregiver stress, with a p-value of 0.03, indicating the result is unlikely to be due to chance under the null hypothesis.”

• “There is statistical significance at the 5% level, but the observed improvement is small; the confidence interval suggests the true effect could range from a modest to a noticeable reduction.”

• “While significant, the practical importance should be weighed alongside effect size and cost.”

Quick note on tools

You don’t have to memorize all the math by heart. Modern software takes care of the heavy lifting. SPSS, R, SAS, and even user-friendly programs like JASP or JASP-based interfaces can compute p-values, confidence intervals, and effect sizes. The key is to interpret them correctly, not to chase a number for its own sake.

Connecting back to the bigger picture

Here’s the thing: numbers are tools, not talismans. The p-value is a helpful signal, but it’s not the entire map. In social work research, we combine statistical signals with ethical judgment, stakeholder input, and a clear eye on real-world impact. The aim isn’t to produce a perfect number but to illuminate what might be worth trying, refining, or scaling in service of communities and families.

A few more friendly contrasts to keep in mind

• Mean versus p-value: The mean tells you where things sit on average; the p-value tells you whether that sitting place is likely reflective of a real effect, not just a random fluctuation.

• Standard deviation versus p-value: The standard deviation shows how spread out outcomes are within the group; the p-value tells you whether the observed pattern across groups is unlikely to happen by chance.

• Correlation coefficient versus p-value: The correlation tells you if two variables tend to move together and in which direction; the p-value (often tied to a test of that correlation) tells you whether that association is likely not due to random variation.

A lightweight mnemonic

If you’re pressed for a quick reminder: p-value = probability of observing the data if there were no real effect. Small p, stronger signal against the idea that “nothing changed.” Bigger p, less evidence against that idea. But always pair that signal with how big the change is and how confident you feel about applying it in the real world.

Closing thoughts: what to carry forward

If you take one idea away, let it be this: the p-value is a compass, not a verdict. It points you toward findings worth a second look, further study, or cautious action. It sits alongside effect size, confidence intervals, and ethical considerations. Used together, these elements help researchers and practitioners ask better questions, make better choices, and ultimately support the people we aim to help.

A last thought on context

Whenever you’re evaluating a study in social settings—whether it’s housing, education, mental health, or family support—pause to picture the people behind the numbers. Think about how without this extra evidence you’d decide, and how with it you might proceed more responsibly. Numbers matter, but the stories they’re tied to matter even more.

If you want a quick recap: a p-value answers, “Could this have happened by chance if there’s no real effect?” A small p-value suggests yes, there’s something real to investigate further. A larger p-value invites caution or a request for bigger or deeper study. And always pair significance with practical significance—because in the end, the goal is to help people, not just to hit a statistic that looks impressive.