This isn't a graph generator. It's just a graph. You can plug data into it and get a graph, I guess, but the same goes for Excel.<p>A misleading graph generator that automatically matched concurrent data sets based on correlation would be quite interesting, but this isn't it.
How 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.<p>There is a famous satirical version of this too: <a href="http://en.wikipedia.org/wiki/File:PiratesVsTemp(en).svg" rel="nofollow">http://en.wikipedia.org/wiki/File:PiratesVsTemp(en).svg</a><p>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.<p>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"
I hear more and more chanting of "correlation does not equal causation!" which is great if your goal is to form a causal model of the world, but there are plenty of insights you can arrive at from correlation alone.<p>For starters in the world of machine learning and predictive analytics, it doesn't really matter if X causes Y so long as X is a consistently good predictor of Y. Maybe powerlines being over someone's home are not the cause of cancer, but if their presence can be used to predict cancer rates that's a good thing.<p>More important imho is the idea of latent or hidden variables. Two things that are clearly correlated but also seem to not have a causal relationship (just as transistors and longevity) may share a latent variable, that may be either non-quantifiable or completely unobservable. For either case measuring outputs that share a common latent variable and thus correlate with each other might be the only way to attempt to measure hidden, non-quantifiable causes.<p>For example employee happiness might be the cause of employee retention. However you can't currently measure or observe 'happiness', but there may be many, seemingly, unrelated employee activities that correlate with retention because they are also driven by this same latent variable. Studying them is the only way to get a quantifiable understanding of this latent cause.<p>tl;dr somethimes correlation is just as important as causation.
Ironically, this graph does an excellent job of showing a correlation that <i>really exists</i> between the two data sets, albeit a non-linear one.<p>The only misleading thing about this graph is the title, which states a causal link with no evidence of one.
Maybe a bit off-topic here, but I think there is a cause-effect relationship between number of transistors and life expectancy. More transistors implies more computing power. More computing power leads to better/faster information processing, including medical information. This leads to faster patient diagnostics, better treatments (pharmaceutical innovations), earlier and more precise health warnings (lab tests an medical equipment), and so on.<p>Faster and better information processing leads also to higher food quality (food processing plants), higher life quality (environmental temperature and humidity control), etc.<p>Germany is a highly industrialized country, so information processing power causes a big social impact.
For causation, you need A/B testing -- real A/B testing (randomized controlled experiments).<p>Not everyone understands the need for A/B testing and not everyone understands how to do A/B testing.<p>I recently worked on modeling to forecast revenues for a large multinational. One of the variables that was essential for an accurate model was whether prior data came from before or after the company hired their current A/B testing guru. With the new guru, revenues rose every week. With the previous "guru", changes by Engineering had no impact on revenues.
Everyone's talking about "correlation vs. causation" but I'm pretty sure the page explicitly states that that's not what this is about:<p>> if its axes are chosen unwisely<p>The graph is misleading because it makes it look like the transistor count and the life expectancy grow at the exact same rate. It pretends there's a linear correlation. And that's the misleading part.
Those who have not read "How to Lie With Statistics" are doomed to reinvent it.<p><a href="https://archive.org/details/HowToLieWithStatistics" rel="nofollow">https://archive.org/details/HowToLieWithStatistics</a>
I feel that many missed my point: It’s not to say causation never implies correlation, that there’s no common source or that latent factors are fiction.<p>In my experience many people believe something is true, just because of “math” or “data”. So, this is basically a variation of the joke that 73.37% of people put more trust into statistics, if the value isn’t rounded. Since you all critically discussed the topic, you are far beyond of what this can little project can teach you.<p>If you have suggestions on how to express this more clearly, please let me know.
I 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.