Understanding effect size in psychological research is really important for anyone working with data.

When we talk about statistical significance, we learn if an effect is there. But statistical significance doesn’t tell us how strong or important that effect is. That’s where effect size comes in. It acts like a bridge between seeing if something is significant and how it can be used in real life.

Why Effect Size Matters

Effect size helps put things into context.

Imagine you did a study comparing two types of therapy for anxiety. Let’s say you found a statistically significant difference (like p < .05).

But if the effect size is small (for example, $d = 0.2$), this means that the difference between the therapies isn’t very big in real life.

On the other hand, if you find a larger effect size (like $d = 0.8$), it shows there’s a big difference. This helps therapists choose the best treatment options for their patients.

What Happens if We Ignore Effect Size?

1. Misleading Conclusions
   If researchers only pay attention to p-values, they might think their study shows important results when it actually doesn’t. For example, if a new school program shows a p-value of 0.03 but an effect size of $d = 0.1$, decisions made based on that could lead to wasting resources.

2. Poor Risk Assessment
   Effect size is key for something called power analysis. This helps figure out how many people to include in a study and understand risks like Type II errors, which happen when we fail to find a real effect. If researchers ignore effect size, they might think they need fewer people than they really do, making it harder to find meaningful effects.

3. Practical Applications
   When we look at interventions or treatments, ignoring effect size can stop us from using research in the real world. Policymakers and practitioners need effect sizes to know if study results matter for their work. A small effect size might mean an intervention isn’t worth the cost.

Conclusion

Effect size isn’t something to think about later; it should be part of the research from the start. By looking at both statistical significance and effect size, researchers can make better conclusions and support evidence-based practices. This way, findings are not just statistically correct, but also useful in real life.

So, remember: the next time you work with data, understanding effect size could be just as important as finding a significant result!