Which option should you use if your data is approximately symmetric or approximately normal?
Mean and standard deviation
If your data is approximately symmetric or approximately normal, you should use measures of central tendency such as mean or median to summarize the data.
The mean is calculated by adding up all the values in the dataset and then dividing by the number of values. The mean is influenced by outliers, meaning it can be heavily skewed by extreme values.
The median, on the other hand, is the middle value in a dataset when it is arranged in ascending or descending order. It is not affected by outliers, so if the data has outliers, it may be a better measure of central tendency than the mean.
In addition, you can use measures of variability like standard deviation or interquartile range (IQR) to describe the spread of the data. Standard deviation measures how spread out the data are around the mean, while IQR describes the range of the central 50% of the data.
Overall, the choice of measure of central tendency and variability will depend on the specific requirements of the analysis and the characteristics of the data.
More Answers:
Understanding the Mean in Statistics: A Guide to Calculating Central TendencyOptimizing Statistical Analysis for Skewed or Outlier-Prone Data Sets: Non-Parametric Tests and Robust Methods
Exploring Data with Box-and-Whisker Plot: Understanding the Median and IQR