# Tests of Statistical Significance

Concepts underlying tests of statistical significance follow:

1.  The purpose of using a test of statistical significance is to determine if the difference in two scores (or the characteristics of the sample is different from the population) is significant or the result of chance fluctuation/or sampling error.

2.  To determine statistical significance, the data are analyzed via a formula and a score is obtained—a t score, chi square score, or F score.

3.  You then determine the degrees of freedom (number of subjects minus one).

4.  Then compare the calculated significance score with the table score in the appropriate probability table using the calculated degrees of freedom. Select the .05 level of confidence. (This would be the minimum level you should accept.)

5.  If the calculated significance score is greater than the one in the table (at the .05 level), then this indicates that the difference in student scores is statistically significant at the .05 level of confidence and not the result of chance fluctuation. This means that the difference in student scores is so great that it could only be obtained by chance only 5% of the time. And 95% of the time, the difference between the student scores is the result of the impact of the independent variable on the dependent variable. Hence, the null hypothesis is rejected.

6.  If the calculated significance score is less than the one in the probability table (at the .05 level), then this indicates that the difference in student scores is statistically insignificant at the .05 level and is the result of chance fluctuation. This means that the difference in the student scores is so small that the independent variable had no impact on the dependent variable. Therefore, 95% of the time, the difference in student scores is the result of chance fluctuation. And only 5% of the time could the independent variable have impacted the dependent variable. Hence, the null hypothesis is accepted.

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