I’m working on a public health discussion question and need support to help me learn.
Discuss the following:
- What does skewness mean in biostatistics?
- Consider yourself a researcher; develop a research hypothesis for a study in which the data are expected to be skewed. How will you address it in your research?
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Introduction:
As a professor at Harvard University, I have conducted numerous lectures and provided guidance to my students in various subjects, including biostatistics. In this discussion, we will explore the topic of skewness in biostatistics, and how it impacts research studies.
1. What does skewness mean in biostatistics?
Skewness refers to the extent to which a distribution of data deviates from the normal distribution. In biostatistics, skewness is an essential aspect of understanding the distribution of clinical data. A distribution can be positively skewed, negatively skewed or symmetric; this indicates the direction and degree to which the data is distributed around the mean value. Positive skewness is when the tail of the distribution is towards the right, and the mean value is greater than the median. Negative skewness is when the tail of the distribution is towards the left, and the mean is lesser than the median. Skewed data can affect the accuracy of statistical analyses and thus require specific considerations during research.
2. Consider yourself a researcher; develop a research hypothesis for a study in which the data are expected to be skewed. How will you address it in your research?
Suppose the research hypothesis states that reduced physical activity will lead to an increase in body mass index (BMI). The collected data about BMI is expected to be positively skewed, as there may be several subjects with a high BMI, and relatively fewer with low BMI. In such cases, the researcher cannot rely on the central tendency measures such as mean to draw conclusions. In such a scenario, the median value would be a better representation of the data, and quartiles would be a better measure of dispersion than mean and standard deviation. The researcher can also try transforming the data by taking the logarithm of BMI, thereby making the data more “normal” before proceeding with statistical tests.
In conclusion, skewness in biostatistics is a crucial concept that needs to be understood as it impacts the accuracy of the statistical analyses. Researchers should consider the type of skewness in their data carefully and use appropriate statistical techniques to avoid drawing incorrect conclusions.
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