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The two categories are equally crucial and thus, they are meant to accomplish various objectives in line with their natures. These two categories apply the concepts or…
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Download file to see previous pages Thus, from that sample, the analysis of the sample speaks volume of the entire population where the sample came from.
Thus, the difference between these two categories is their functions; descriptive statistics is concerned with the spread and centrality of data whereas inferential statistics is concerned with generalizability of the data set. As much as the two categories use similar aspects such as mean, standard deviation, the focus of the two are extremely different. Inferential statistics always start with a sample and end up inferring the entire population using similar calculations as that of descriptive statistics.
Descriptive statistics is only useful in cases whereby one do not wish to extend the statistics results to a larger group or population whereas inferential statistics is used when one wishes to make predictions and inferences on a particular population through the observations from the sample. Thus, the two categories can be used together is cases where a data set has to be fully described and inferences or rather predictions from the available observations made.
Thus, degrees of freedom are crucial in statistics due to the following reason: It is deemed to be a measure of how certain the sample is a representative of the whole population. Therefore, when the degrees of freedom are more, the level of certainty in the accuracy of population sampling is high.
In addition, when it comes to fitting models to a given data, degrees of freedom are considered independent parameters. Thus, an accurate model is described by more independent parameters which are as a result of more degrees of freedom.
3. Explain what a post hoc test is and why they are important. Provide an example of when you might use a conservative, a moderate, and a very liberal post hoc test. Differentiate between the post hoc tests used in ONEWAY ANOVA and Multiple-Factor ANOVA
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