I'm not aware of any right way to use statistic other than being honest and aware regarding the limitations and assumptions surrounding the data.
We should be precise in understanding basic terms used in statistics like mean, median and mode.
What doe Mean, Median, Mode mean?
Mean of any data is only representative if you have a normal distribution.
So the question to ask here is: were there any outliers/extremes? E.g. if you have nine 3 year olds in a room and one 93 year old, the mean is 12 years old. That's not necessarily a true representation.
Median is the middle of the data set. So for:
2, 5, 6, 13, 15
0, 3, 6, 99, 4915
The median is 6 for both sets!
It's the middle number of the range with two other numbers (50% of the data) on either side.
Hopefully you can see the limitation here because 6 would be the median for both of the sets even though the extremes are very different.
Asking for both the mean and the median helps build a better picture - as well as awareness of outliers.
Mode is the number in your data that comes up the most. So in the first example above where you had nine 3 year olds in a room and one 93 year old, the mode would be 3.
This is because you have the most number of 3 year olds in your data set.
So knowing all three, mean, median and mode, is usually more helpful.
Next, the Categorisation Method
With the basics out of the way... the more important point for us is the categorisation method.
When data is categorised, especially sociological/political data, ideological presuppositions almost always underpin that data... and that can change everything!
Was it five Muslims? Or Five Men? Or Five secular liberal Muslims? Or five black people? Or just five people?
What percentage was it? 100 people sounds a lot but not if your total data set is 7 billion. Then 100 is tiny.
Over what period was the data measured? Over 1 month, 1 year, 10 years or 100 years?
Does the fact we are looking at only men or only women really have anything to do with the issue at hand?
For example, I see endless red herrings when it comes to data being used to highlight the "differences" that exist between men and women. Whereas often there are other reasons why such differences exist that need further exploration. The Canadian Psychologist Jordan Peterson's famous discussion with Channel 4's Kathy Newman over the pay gap illustrates this well.
How the data has been categorised and the underlying assumptions are important - especially in an ideologically charged environment where liberal or conservative ideas are being promoted.
We need to be objective and know when we are being ideologically unbiased (perhaps at the start of a relationship) or consciously ideologically driven - when we want to push our message to a large audience (the masses) - without being dishonest, of course. We don't want to intentionally convey an inaccurate story in order to win an argument.
We should not be manipulating statistics to influence people to think like us by any means necessary - instead we should seek to raise the awareness and level of thinking - so we can all arrive at the truth, whatever that may be.
Finally, another frequently occuring issue I've seen, even when the data is presented accurately, the data does NOT give you a solution to the problem. What I see repeatedly is a secular-liberal solution being assumed.
Let's say the data shows a particular city has 10 times more guns and 10 times more gun crime compared to its neighbouring city. Does that mean reducing guns is the obvious and automatic solution? Most certainly not.
This is where values and ideology play a role. You'll often hear: "the data suggests such and such solution". Data never suggests anything by itself. The interpreter is doing the suggestions.
A civilisation is built upon a set of values that are used to get to a solution. These values, which form the foundations of the civilisation's ideology, play an important role in the interpretation of the world around us.
Solution don't emerge from unbiased, objective data alone - if you believe this then you're probably unaware that you've been assimilated into your society's ideology.
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