Misleading Graph Generator
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Misleading Graph Generator
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Re: Misleading Graph Generator
#2God says...
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shudder named Pontitianus inflicted now recommended digest
scarce-remembered wroth felicity indigested Ambition Amen
inhabited Maker Court intercession
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How about human motor skills over thousands of years. Handwriting control in millimeters?
God says... dir divisions overwhelmed latter scatter littles adversities candle steer 'may Thyself courage
Re: Misleading Graph Generator
#3How is this graphs fault? The only fault the graph has is that it clearly displays the data, the problem is in the idea that is represented. It's formally called "Correlation does not imply causation" and it's fault of the person who is suggesting it.
There is a famous satirical version of this too: http://en.wikipedia.org/wiki/File:PiratesVsTemp(en).svg
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It has nothing to do with graphs, graphs are are just visual tools and people reading data from them are supposed to evaluate it just like reading data from any other tool and not jump into conclusions.
What the graph in question suggests is that both "transistor count" and "average life expectancy in germany" have risen trough time and if you are reading this as "rising transistor count increases life expectancy" it's your fault. Why not read it like "from 1971 to 2011 both transistor count and life expectancy increased steadily, maybe because of the advances in technology - we should look into it, it's too early to say anything"
Re: Misleading Graph Generator
#4I think this is a very good idea. Lots of people still mix up correlation and causation. Either this tool will make them understand what this is wrong, either they will enjoy finding lots of weird relations between data sources.
Re: Misleading Graph Generator
#5How is this graphs fault? The only fault the graph has is that it clearly displays the data, the problem is in the idea that is represented. It's formally called "Correlation does not imply causation" and it's fault of the person who is suggesting it. There is a famous satirical version of this too: http://en.wikipedia.org/wiki/File:PiratesVsTemp(en).svg It has nothing to do with graphs, graphs are are just visual to…
> It has nothing to do with graphs, graphs are are just visual tools.
To make a graph, one has to make decisions, these decisions can be legitimate or they can be biased. Biased decisions lead to misleading graphs.
The same goes for other types of content such as news articles, product reviews, war photos, etc.
Edit, to illustrate better: the author of the graph chose to overlap data on a logscale (transistor count) with data on a linear scale (life expectancy). The resulting graph shows two similar curves, that suggests that the two are strongly correlated. That's what I call a biased decision.
Re: Misleading Graph Generator
#6Re: Misleading Graph Generator
#7Ironically, this graph does an excellent job of showing a correlation that really exists between the two data sets, albeit a non-linear one.
The only misleading thing about this graph is the title, which states a causal link with no evidence of one.
Re: Misleading Graph Generator
#8Ironically, this graph does an excellent job of showing a correlation that really exists between the two data sets, albeit a non-linear one. The only misleading thing about this graph is the title, which states a causal link with no evidence of one.
Yeah, I think this is completely off-base in blaming the axes. The axes are perfectly fine in showing the actual correlation that's present here (almost certainly due to a third common causal factor, of course).
There's no really magically "unbiased" way of choosing axes. There seems to be a popular view recently that you should always start your axes at zero, I assume as a backlash to some graphs magnifying very small differences by choosing zoomed-in axis values that visually exaggerate variation. That isn't really an absolute truth either, since interesting data regions are not always near zero. For example, if you graph temperature variation in different cities staring at 0 K, you can make it look like essentially all habitable cities have around the same temperature, somewhere in the range of 250-300 K give or take. Of course 250 K versus 300 K is a huge difference to human perception of temperature, while the entire range 0-200 K is more or less irrelevant when discussing weather, so starting your axis at 0 would be a poor choice. In this case that would be the biased choice, intended to visually minimize actually important variation by choosing an unreasonably low starting point for the Y axis.
Re: Misleading Graph Generator
#9Ironically, this graph does an excellent job of showing a correlation that really exists between the two data sets, albeit a non-linear one. The only misleading thing about this graph is the title, which states a causal link with no evidence of one.
Yeah, I think this is completely off-base in blaming the axes. The axes are perfectly fine in showing the actual correlation that's present here (almost certainly due to a third common causal factor, of course). There's no really magically "unbiased" way of choosing axes. There seems to be a popular view recently that you should always start your axes at zero, I assume as a backlash to some graphs magnifying very sma…
> (almost certainly due to a third common causal factor, of course).
..or absolutely certainly associated so remotely that correlation is purely accidental and found only by carefully cherry picking data sources, ranges, functions to massage the data (log with the right base) and axis ranges. To sum up ... not meaningful.
You could find similar correlation between ocean temperatures and lottery numbers but you'd have to precisely adjust so many inputs that the correlation would be not so much found as constructed.
Re: Misleading Graph Generator
#10Earlier quoted context omitted.
Yeah, I think this is completely off-base in blaming the axes. The axes are perfectly fine in showing the actual correlation that's present here (almost certainly due to a third common causal factor, of course). There's no really magically "unbiased" way of choosing axes. There seems to be a popular view recently that you should always start your axes at zero, I assume as a backlash to some graphs magnifying very sma…
> (almost certainly due to a third common causal factor, of course). ..or absolutely certainly associated so remotely that correlation is purely accidental and found only by carefully cherry picking data sources, ranges, functions to massage the data (log with the right base) and axis ranges. To sum up ... not meaningful. You could find similar correlation between ocean temperatures and lottery numbers but you'd have…
"log with the right base" doesn't make any difference. That's a linear mapping. For instance:
2log x = log x / log 2 = ln x / ln 2
See, for example, http://www.purplemath.com/modules/logrules5.htm, or derive it from the axioms.