Normal distribution takes on a bell curve because of the variation in the distribution of data across the axis (Holmes et al., 2017). One of the variables that would have a normal distribution curve in medicine would be the systolic blood pressure readings in healthy people. For instance, blood pressure has a mean of 112mmHg and with a standard deviation of 10mmHg. It is important to note that even with a normal distribution, variables such as blood pressure could change their distribution curve because of additional factors. These factors include stress levels, activity levels, diet changes, and medication, among others.
Another variable in medicine that would have a normal distribution curve would be height. Height follows a normal distribution curve because there is a mean height, which is often about 1.77m for males. While there are very tall males and very short males, most of the population’s height is distributed around the mean of 1.77 meters for an adult person. Most heights would be clustered around this mean, which means their height is either shorter or longer. The height distribution along the bell curve would be affected by significant factors such as genetics, gender, age, race, and physical changes such as amputation. Therefore, plotting a histogram of height, with height on the x-axis and the frequency on the y-axis, one would achieve an asymmetrical curve because of the skewed distributions. Skewed distributions occur because of the different factors that influence the variables (Balakrishnan, 2014). The same case applies to the blood pressure distribution curve, which is generally normal. However, on a histogram, the distribution is asymmetrical and skewed because of factors such as stress levels, weight, and physical activity levels, which affect the blood pressure.
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References
Balakrishnan, N. (2014). Methods and applications of statistics in clinical trials . Wiley.
Holmes, A., Illowsky, B., & Dean, S. (2017). Introductory business statistics . Samurai Media Limited.
