## What is meant by level of significance?

The significance level, also denoted as alpha or α, is a measure of the strength of the evidence that must be present in your sample before you will reject the null hypothesis and conclude that the effect is statistically significant. Compare your p-value to your significance level.

**What is an example of practical significance?**

Example: Commute Times Because the null hypothesis was rejected, the results are said to be statistically significant. Note: The pooled standard deviation should always be between the two sample standard deviations. The mean commute time in Atlanta was 0.402 standard deviations greater than the mean commute time in St.

**What does significant difference mean in statistics?**

A statistically significant difference is simply one where the measurement system (including sample size, measurement scale, etc.) was capable of detecting a difference (with a defined level of reliability). Just because a difference is detectable, doesn’t make it important, or unlikely.

### What are non-significant results?

The most reasonable way to describe non-significant results is probably that the study did not find convincing evidence against the hypothesis that that the treatment effect was zero.

**What is statistical power and why is it important?**

Statistical power is the probability of a hypothesis test of finding an effect if there is an effect to be found. A power analysis can be used to estimate the minimum sample size required for an experiment, given a desired significance level, effect size, and statistical power.

**What does it mean if a variable is not statistically significant?**

If the p-value for a variable is less than your significance level, your sample data provide enough evidence to reject the null hypothesis for the entire population. On the other hand, East is not statistically significant because its p-value (0.092) is greater than the usual significance level of 0.05.

#### How do you know if a t test is significant?

Compare the P-value to the α significance level stated earlier. If it is less than α, reject the null hypothesis. If the result is greater than α, fail to reject the null hypothesis. If you reject the null hypothesis, this implies that your alternative hypothesis is correct, and that the data is significant.

**What does it mean that the results are not statistically significant for this study?**

This means that the results are considered to be „statistically non-significant‟ if the analysis shows that differences as large as (or larger than) the observed difference would be expected to occur by chance more than one out of twenty times (p > 0.05).

**How do you tell the difference between statistical significance and practical significance?**

While statistical significance shows that an effect exists in a study, practical significance shows that the effect is large enough to be meaningful in the real world. Statistical significance is denoted by p-values whereas practical significance is represented by effect sizes.