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The Statistics document describes descriptive and inferential statistics. Descriptive statistics allow you to describe what happens within datasets, but inferential statistics allow you to ascertain whether differences or associations between datasets are significant. (Statistics Briefing document, n.d.). An example of a descriptive statistic would be using the formula for calculating the mean to calculate the average of the dataset. The only thing you can conclude as a researcher is that the mean responses are different from one another. You are not able to decipher whether the difference is statistically significant. .Inferential statistics, on the other hand, helps you to make that determination (statistical significance), you would have to use inferential statistics. For example, you examine the test scores of three different groups using three different models for teaching the calculation of descriptive statistics.  You can make conclusions about which method produces better performances after consulting the critical values for F, depending on whether any results were statistically significant.  I have no statistics or research methods background, so most of the material discussed in this document is new to me.

The Reliability ERIC digest website discussed the various different definitions of reliability by many different people. Reliability has been defined in different ways by different authors. Perhaps the best way to look at reliability is the extent to which the measurements resulting from a test are the result of characteristics of those being measured. The last and final document “Reliability and Validity” discusses construct and content validity.

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