A continuous variable is a variable that takes an infinite number of possible values, usually obtained by measurements such as weight and height. Answers in a continuous variable are distributed over a long set of figures, and the distribution is normal unless the data is skewed or has outliers. For example, when measuring time, we can say that it’s ten a.m., thirty-three minutes and ten seconds. The best measure of central tendency to use in the continuous variable is the mean.
Mean is the total sum of the numbers in a set of data divided by the values in the set of data. It measures the most common number in the set of values. Its advantage is that every number in the data set is taken into account ( Schaffner, et al. 2012) . However, mean is not appropriate for highly skewed data and outliers. Skewed data is data that is predisposed to have a long extension at the end of a side. Outliers are those numbers which are uncommon in contrast to the rest of the data.
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If data is highly skewed in a continuous variable, it is advisable to use the median. Median measures the average score of a set of data when it has been arranged from the smallest to the largest. If the value of the numbers is even, one takes the two values that are in the middle and find their average. If the values are odd, one just takes the middle one.
The mode is another method that can be used though not very effective. It considers the value that appears the most in a set of data. The reason being there can be more than one value that has a high frequency. Also, if the value of the mode is far away from the other of data, then it does not give an appropriate measure of central tendency.
References
Samuels, M. L., Witmer, J. A., & Schaffner, A. (2012). Statistics for the life sciences . Pearson education.
