Statistical significance is a measure of how likely it is that an observed difference between two groups or variables is due to chance. In the context of research, a statistically significant result is one that is unlikely to have occurred by chance alone.
What is a p-value?
The p-value is a probability value that is calculated using a statistical test. It tells us the probability of obtaining a result as extreme as the one we observed, assuming that the null hypothesis is true. The null hypothesis is the hypothesis that there is no difference between the two groups or variables being compared.
How does the p-value relate to statistical significance?
A p-value of less than 0.05 is generally considered to be statistically significant. This means that there is a less than 5% chance of obtaining a result as extreme as the one we observed, assuming that the null hypothesis is true.
Interpreting the statistical results in the excerpt
The excerpt from the hypothetical research article reports the results of an independent samples t-test. The t-test is a statistical test that is used to compare the means of two independent groups. The p-value for the t-test is 0.034, which is less than 0.05. This means that the results of the t-test are statistically significant.
What do the statistically significant results mean?
The statistically significant results mean that it is unlikely that the observed difference in memory-test performance between the two groups is due to chance alone. In other words, the results suggest that caffeine consumption has a negative effect on memory performance.
Conclusion
Statistical significance is an important concept in research. It is a measure of how likely it is that an observed difference between two groups or variables is due to chance. A p-value of less than 0.05 is generally considered to be statistically significant.
Example
Imagine that you are conducting a research study to compare the effectiveness of two different drugs for treating a certain disease. You randomly assign participants to receive either drug A or drug B. After a period of time, you measure the participants’ improvement in symptoms. You then conduct a statistical test to compare the mean improvement in symptoms between the two groups.
If the p-value for the statistical test is less than 0.05, then the results are considered to be statistically significant. This means that it is unlikely that the observed difference in mean improvement in symptoms between the two groups is due to chance alone. In other words, the results suggest that one drug is more effective than the other.
It is important to note that statistical significance does not necessarily mean that a result is important or meaningful. For example, the results of the study described above might be statistically significant, but the difference in mean improvement in symptoms between the two groups might be so small that it is not clinically meaningful.
It is also important to note that statistical significance does not mean that a result is true. It is possible to obtain a statistically significant result even if the null hypothesis is true. This is known as a type I error.
However, statistically significant results are important because they provide evidence that there is a real effect or relationship between the variables being studied. When a result is statistically significant, we can be more confident that the effect or relationship is not due to chance alone.
