One of the main reasons we invest our time into research (whether performing the science or reading the reports) is to understand how to avoid problems. We want to know what we can do to improve chances of a good outcome and have the best health. We want to know what treatments are the most […]
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We talked about a random sample last week. Today we are going to talk about randomized controlled trials. The use of the term randomized in this context does not refer to the sampling method. Randomized controlled trials use convenience sampling, meaning they recruit whoever is available and meets the enrollment criteria. Because of this, randomized […]
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On Wednesday we talked about why sample size matters. Today I want to focus on how the sampling method affects the data. Basically, there are two ways to obtain a sample. One way is to randomly select members of the population, and the other is to use whomever is available. To understand the difference, I […]
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On Monday we talked about regression techniques and I explained that finding a statistically significant result would be difficult because of our sample size. Today we’ll explore how sample size affects the results you will get. To understand the importance of a sample size, you need to think back to our discussion about p-values and […]
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Like the other statistical techniques we’ve talked about, regression techniques allow us to examine the relationship between two variables. But the regression techniques go a step beyond the Chi-Square and T-Test because they allow us to examine the relationship of multiple variables. Unlike the previous tests, regression allows you to find the correlation of multiple […]
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On Wednesday we used a T-Test to see if there was a difference in mean labor time between women who worked as a doula for income and those that worked as a doula for hobby. The next obvious question is, what is a T-Test and why did we choose to use it? Remember back to […]
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On Monday we talked about how p-values tell us the probability of obtaining the result if the null hypothesis (the status quo) is true. Today we turn our attention to the confidence interval and the additional information using a confidence interval provides. There are two things you need to remember to make sense of a […]
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We’ve talked about data and the descriptive statistics. We’ve talked about the measures we use and stratifying by populations and sub populations to assess disparities. We talked about the types of studies and last week we moved into our first statistical test of a hypothesis, the chi-square. Today we will talk about how we know […]
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In this study, Pregnancy outcome in women with previous one cesarean section, the authors did many statistical comparisons of the data. One comparison is easily understood with a chi-square – does having a previous vaginal delivery increase the success rate of VBAC?
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Before we move into our example study we need to spend a few minutes on the difference between risk and odds. It can be difficult to understand, but it is important for interpretation of studies. When research presents risk, what is being presented is the number of people with the selected characteristic divided by the […]
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