Understanding Statistical Significance vs. Practical Significance
In psychology research, it’s important to understand two key ideas: statistical significance and practical significance. They might sound similar, but they mean different things. Let’s break them down.
Statistical significance is a way to tell if the results we see in our data are likely real and not just random luck.
When researchers test a hypothesis, they often use something called p-values. A common rule of thumb is to look for a p-value of less than 0.05. This means there's less than a 5% chance that the results happened by chance.
For instance, if a study finds that a new treatment helps reduce anxiety, and the p-value is 0.03, it suggests that the treatment really has an effect. It isn’t just random changes in the groups being studied.
Practical significance is different. It looks at how important the findings are in the real world. Even if a result is statistically significant, it doesn't always mean it's important.
Let’s say that same treatment does reduce anxiety, but only by 0.5 points on a scale of 10. Researchers might wonder if this small change is enough to make the treatment worth using in real life. Here, we consider the effect size, which shows how strong the treatment’s impact is. The bigger the effect, the more important it usually is.
Statistical Significance: Focuses on whether we can trust the results; it doesn’t consider how big or important the effect is.
Practical Significance: Looks at how relevant and useful the results are in real life; it evaluates if the findings make a meaningful difference.
Imagine a study on a new way to teach kids. The results show a statistically significant increase in test scores with a p-value of 0.02. But if the average increase is only 1 point out of 100, teachers might think that isn’t enough to change how they teach.
On the other hand, if another teaching method raises test scores by 10 points, but doesn’t reach statistical significance (maybe because the sample size is too small), it could still be worth exploring further.
It’s really important to know the difference between statistical significance and practical significance in psychology research. Researchers should not only give p-values but also explain how their findings are relevant in the real world. This way, the results can have a bigger impact beyond just numbers and tests.
Understanding Statistical Significance vs. Practical Significance
In psychology research, it’s important to understand two key ideas: statistical significance and practical significance. They might sound similar, but they mean different things. Let’s break them down.
Statistical significance is a way to tell if the results we see in our data are likely real and not just random luck.
When researchers test a hypothesis, they often use something called p-values. A common rule of thumb is to look for a p-value of less than 0.05. This means there's less than a 5% chance that the results happened by chance.
For instance, if a study finds that a new treatment helps reduce anxiety, and the p-value is 0.03, it suggests that the treatment really has an effect. It isn’t just random changes in the groups being studied.
Practical significance is different. It looks at how important the findings are in the real world. Even if a result is statistically significant, it doesn't always mean it's important.
Let’s say that same treatment does reduce anxiety, but only by 0.5 points on a scale of 10. Researchers might wonder if this small change is enough to make the treatment worth using in real life. Here, we consider the effect size, which shows how strong the treatment’s impact is. The bigger the effect, the more important it usually is.
Statistical Significance: Focuses on whether we can trust the results; it doesn’t consider how big or important the effect is.
Practical Significance: Looks at how relevant and useful the results are in real life; it evaluates if the findings make a meaningful difference.
Imagine a study on a new way to teach kids. The results show a statistically significant increase in test scores with a p-value of 0.02. But if the average increase is only 1 point out of 100, teachers might think that isn’t enough to change how they teach.
On the other hand, if another teaching method raises test scores by 10 points, but doesn’t reach statistical significance (maybe because the sample size is too small), it could still be worth exploring further.
It’s really important to know the difference between statistical significance and practical significance in psychology research. Researchers should not only give p-values but also explain how their findings are relevant in the real world. This way, the results can have a bigger impact beyond just numbers and tests.