Answer: mean < median
The mean is smaller than the median
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Step-by-step explanation:
A left-skewed, or negatively skewed, distribution is one where the left tail is longer than the right tail. There are relatively few small outliers to the left of the main cluster of every other value. Those small outliers will pull on the mean to make the mean smaller than it should be (if we were to ignore the outliers). Think of the mean being affected by the "gravitational" pull of those small outliers. In reality, every data point has that pull on the mean. It's just that the outliers pull the mean down so to speak.
In contrast, the median is never affected by outliers. This is why the median is used for something like home prices so that you get a better picture of the center even if there are multimillion dollar mansions being included. Because the median isn't changed by those smaller outliers, we have the mean be smaller than the median. So mean < median.
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Extra info (optional section):
If the data was skewed to the right, aka positively skewed, then we'd have the reverse situation and we'd have mean > median. The large outlier(s) pull on the mean to be larger than it should be which makes it larger than the median.
If there's no skew at all, we consider the distribution to be perfectly symmetric. In such a case, mean = median. Both values measure the same center point.