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After calculating the mean and standard deviationvalues each value of the independent variable in the data, these are a few common tests that are used to further analyse the data and highlight its significance:

1) Pearson Correlation Coefficient

- This is to test for a strong/weak positive/negative correlation between the independent variable and the dependent variable. However, correlation does not necessarily imply causation.

2) t-test

- This post-hoc test is used to determine the level of significance of the difference between two sets of data.

3) Chi2 test

- This test tests for whether the difference in Expected and Observed values are significant or not.

4) Analysis of variance (ANOVA)

- This is like a massive t-test to test an entire set of data, without inflating the error of the analysis results. This is usually coupled with Tukey's Honest Significant Difference test.

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Maude Kulas

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After calculating the mean and standard deviationvalues each value of the independent variable in the data, these are a few common tests that are used to further analyse the data and highlight its significance:1) Pearson Correlation Coefficient- This is to test for a strong/weak positive/negative correlation between the independent variable and the dependent variable. However, correlation does not necessarily imply causation.2) t-test- This post-hoc test is used to determine the level of significance of the difference between two sets of data.3) Chi2 test- This test tests for whether the difference in Expected and Observed values are significant or not.4) Analysis of variance (ANOVA)- This is like a massive t-test to test an entire set of data, without inflating the error of the analysis results. This is usually coupled with Tukey's Honest Significant Difference test.


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After calculating the mean and standard deviationvalues each value of the independent variable in the data, these are a few common tests that are used to further analyse the data and highlight its significance:1) Pearson Correlation Coefficient- This is to test for a strong/weak positive/negative correlation between the independent variable and the dependent variable. However, correlation does not necessarily imply causation.2) t-test- This post-hoc test is used to determine the level of significance of the difference between two sets of data.3) Chi2 test- This test tests for whether the difference in Expected and Observed values are significant or not.4) Analysis of variance (ANOVA)- This is like a massive t-test to test an entire set of data, without inflating the error of the analysis results. This is usually coupled with Tukey's Honest Significant Difference test.


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