- Recognize Type I and Type II errors and their consequences.
Statistical Significance
A hypothesis test involves collecting data from a sample and evaluating the data. The actual test begins by considering two hypotheses called the null hypothesis and the alternative hypothesis. These hypotheses contain opposing viewpoints. The statistician then decides if there is sufficient evidence to reject the null hypothesis using the P-value and analyses of the data. However, when you perform the hypothesis test, there are possibilities of making errors depending on the actual truth (or falseness) of the null hypothesis and the data we collect and analyze.
Let’s explore the significance of our conclusions based on the data collected.
[Trouble viewing? Click to open in a new tab.]
statistical significance
If a hypothesis test results in rejecting the null hypothesis because the P-value is less than the significance level, we say we have statistical significance. This means there is enough evidence against the null hypothesis to reject the null hypothesis.