We are working on health survey project which has more than 100000 participant and we are confused to use mean or median?
Please help us.
Data are not following normal distribution.
The arithmetic mean is an index of analytical position; the median is a position index drops. I do not understand your confusion.
Both mean and median can be computed. Forsymmetrical curve both coincide as in normal distributed variable. It is not necessaey that only one of the two can be calculated for a data set. However, for ordinal data only median can be calculated. If both mean and median are computed then the values needs to be interpretd accordingly.
Both mean and median can be computed. Forsymmetrical curve both coincide as in normal distributed variable. It is not necessaey that only one of the two can be calculated for a data set. However, for ordinal data only median can be calculated. If both mean and median are computed then the values needs to be interpretd accordingly.
Hi, KrUNAL Dholiya
There is no context in your question.
Mean or median for what doing?
What kind of data?
This may help to answer.
Dr Pinilla,
Under a health project, we have screen blood of more than 1,00,000 people in rural area of India. We are analyzing data with various test. Question raised, use of parametric tests or non parametric tests. As data are not normally distributed (as per KS test for normality). Even visible skwedness is
Is it good to use parametric test for my data or non parametric test?
Many researcher have published result using parametric tests, irrespective of distribution. Many researcher says we should not apply parametric tests to skwed data.
In this case, it may be useful to calculate, not only the median, but a set of percentile points; for example: 0.1%, 0.5%, 1%, 5%, 10%, 20%, 50%, 80%, 90%, 95%, 99%, 99.5%, 99.9% points.
I still do not know if you just want to set some benchmarks or if you have additional research goals.
With this amount of data, I do not think it would be useful to apply inferential procedures to either means or medians.
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