Hello people! I have not written a blog in a good minute.  I was inspired this morning to write about the steps leading up to analysis. So I have consulted quite a few students up to this point. And I made the observation that some research questions are formulated without considering how to collect the data. I am not speaking what the data will look like when you finally analyze it. Rather we should focus on random versus non-random sampling. And the key issue is this thing called bias. When you try to show statistical significance, you want the results to represent what is actually happening in the population. Introduce bias in your data, distort the claim in your paper. Random sampling ensures that the data is collected without bias. There are certain statistical tests that you can not run if your data was not collected at random.

I keep throwing around this work random. What I am I talking about here? You want your study participants to have an equal likelihood of being selected for the study. Probability sampling ensures each person in your sample had an equal likelihood of being chosen for the study. Now if you have a convenience sample, data collected at a particular place and time- you can not generalize about the larger true population. Convenience sampling is not based on probability and you can not analyze the data using parametric tests.

I suggest getting familiar with the way you collect the data before the situation is irreversible.

 

-Moore to follow Amy

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