Statistical Checks: Mean and Median Analysis Statistical checks are a crucial part of data analysis, used to assess the central tendency and variability of a...
Statistical checks are a crucial part of data analysis, used to assess the central tendency and variability of a dataset. Two commonly used measures are mean (average) and median, both representing the "center" of the data.
Mean:
Imagine a weighted average, with each data point having a specific weight based on its importance.
The mean is calculated by adding up all the weights and then dividing by the total weight.
It is a sum of all values divided by the total number of values in the dataset.
For example, in the following dataset of heights (in cm): 165, 170, 160, 175, 180, the mean height is (165 + 170 + 160 + 175 + 180) / 5 = 175 cm.
Median:
Imagine flipping the data upside down and arranging it in order from smallest to largest.
The median is the middle value in this flipped order, if there are an even number of values.
If there is an odd number of values, the median is the average of the two middle values.
For example, in the following dataset, the median is 165, as it is the middle value in the flipped order.
Comparing mean and median:
Mean: is generally more robust to outliers than the median. This means that it is less affected by extreme values in the dataset.
The median is more sensitive to outliers, as it is based on the order of the values.
In conclusion:
Statistical checks help us understand the center and spread of a dataset.
Knowing the mean and median allows us to compare and summarize datasets of different sizes and make predictions about the data