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Rejecting the Null Hypothesis,| continued to work on scenario 3. | cleaned the data by filtering for science score results. | rejected the null hypothesis after conducting the correlation test. The p-value from the F-test conducted was 0. This is less than the significance value of 0.05, which is a common, thowgh arbitrary, threshold for determining statistical certainty (Power, 2015). Since the result is statistically sagnificant, the null hypothesis can be rejected (Powner, 2015). Despite this, there was a very weak positive correlation of 0.087 between years of expenence and test scores.Post-hoc Test,| believe that | hawe conducted checks for cormelation and significance in the assumptions tests. Therefore, post-hoc tests are not needed. These are typically conducted when comparing 2 or more groups. Future The results of the mini-progect may inspire future researchers to look into comparing not comelation, but causation with @ quasi-experimental study. The results of the F-test suggest significance in the variances of the data. Do other factors have an effect on science scores besides teachers' years of experience? Researchers could also widen the sample size and include other similar districts or pull samples at the state or country level. (Powner, 2015) Different Steps if bone Again.lf had to do the mini-study again, | would use the sample of the whole first to look at the conrelation of all the subjects' teachers' years of expenence and test scores. | may then compare grade levels to teachers' years and test scores through AMOVA, Comparing groups based on a key vaniable could help answer a different related question: Is there a statistically significant difference in the mean Science Test Score Year 2 among the different grade levels? (McGregor, 2019) Different Design| would want to change the design to a causal-comparative study. However, this is due to the fact that correlation was suggested to mot be present by the results of the analyses. tis also based on the desire to compare more than two variables. A goal of quantitative research is to seek out potential cause-and-effects, and a different design could explore this more directly than a comelational approach (McGregor, 2019)