Its the one most commonly used but outliers can seriously distort the mean.
The mode.
The median, as long as you don't want to do any serious statistical testing.
There is no universal best. The mode is sometimes mentioned as a measure of central tendency but it is not really one. For example, if studying rolls of a die, the mode has nothing whatsoever to do with central tendency. However, it is the only summary measure that makes sense when the observed variable is nominal or categoric. For example, if the data are about the colours of cars, the mean or median colour makes no sense. The mean and median have advantages over the other in different circumstances. The Central Limit Theorem and Normal approximation favour the mean but the unrestricted mean is vulnerable to outliers.
If you want to ask questions about "this situation", then I suggest that you make sure that there is some information about the situation in the question.
box-and-whisker plot
Median
mean
The mode.
you need to be more specific with your question!!
The median.
The mode.
by getting the mean and multiply by the median
never * * * * * When the data are qualitative. The mean and median are unusable in such cases and the mode is the only sensible measure.
The mean may be a good measure but not if the data distribution is very skewed.
The median, as long as you don't want to do any serious statistical testing.
There is no universal best. The mode is sometimes mentioned as a measure of central tendency but it is not really one. For example, if studying rolls of a die, the mode has nothing whatsoever to do with central tendency. However, it is the only summary measure that makes sense when the observed variable is nominal or categoric. For example, if the data are about the colours of cars, the mean or median colour makes no sense. The mean and median have advantages over the other in different circumstances. The Central Limit Theorem and Normal approximation favour the mean but the unrestricted mean is vulnerable to outliers.
The arithmatic mean is not a best measure for central tendency.. It is because any outliers in the dataset would affect its value thus it is considered not a robust measure.. The mode or median however would be better to measure central tendency since outliers wont affect it value.. Consider this example : Arithmatic mean dan mode from 1, 5, 5, 9 is 5.. If we add 30 to the dataset then the arithmatic mean will be 10 but the mode will still same.. Mode is more robust than arithmatic mean..