Final answer:
The term 'statistically significant' pertains to findings from a statistical test that are unlikely to have occurred by chance, indicating a real effect. This significance is typically measured by comparing the p-value to a predetermined alpha level, often set at 0.05, where a lower p-value than α suggests rejecting the null hypothesis.
Step-by-step explanation:
The term “statistically significant” is synonymous with the assessment that the findings from a statistical test are unlikely to have occurred by random chance alone, and hence suggest a genuine effect or relationship present in the population being studied. In hypothesis testing, statistical significance is often determined by comparing the p-value to a predetermined α level (alpha level), commonly set at 0.05 (5 percent). When the p-value is lower than the α level, the result is considered statistically significant, implying that there is sufficient evidence to reject the null hypothesis.
Statistical significance is commonly achieved by either using the p-value method or a table of critical values. These two methods, while different in procedure, lead to the same conclusion about the statistical significance of the data. If the p-value is less than α, as in the example provided where a p-value of 0.0187 is smaller than α of 0.05, the conclusion is to reject the null hypothesis with confidence.
To enhance the power of a test and thus the confidence in rejecting the null hypothesis, statisticians may increase the sample size or use a lower alpha level to determine significance. The goal of these adjustments is to minimize the chances of both Type I errors (incorrectly rejecting a true null hypothesis) and Type II errors (not rejecting a false null hypothesis).