Final answer:
Statistical significance refers to the likelihood that the observed results are due to a real effect rather than chance. In hypothesis testing, the level of significance (also known as alpha) represents the threshold below which we reject the null hypothesis. If the outcome of a test is statistically significant at the 0.01 level, it means that the observed results are highly unlikely to be due to chance and we can reject the null hypothesis at that level. However, at the 0.05 level, which is a less strict threshold, the results may or may not be statistically significant.
Step-by-step explanation:
Statistical significance refers to the likelihood that the observed results are due to a real effect rather than chance. In hypothesis testing, the level of significance (also known as alpha) represents the threshold below which we reject the null hypothesis.
In this case, if the outcome of a test is statistically significant at the 0.01 level, it means that the observed results are highly unlikely to be due to chance and we can reject the null hypothesis at that level. However, at the 0.05 level, which is a less strict threshold, the results may or may not be statistically significant.
So, what can be said about the statistical significance at the 0.05 level is that it is possible that the results may still be statistically significant, but it would require a higher level of evidence to reject the null hypothesis compared to the 0.01 level.