>1). There is a relationship in your data
>2). There isn't a relationship in your data
From there, you can then setup further experiments to better understand why there is or isn't a relationship -- and perhaps find the opposite is true!
It's the same reason you don't assume a theory is "true;" you don't conclude "that higher unemployment in Canada vis-a-vis the US is almost certainly driven by a lower supply of jobs, rather than as you posit a lower demand for employment" (GGP), because that's bad science!
What the GGP did is create an inference -- one based on misread data and faulty assumptions about scientific methods -- and expressed it as de facto. Now, in order for his inference to have weight, he must either support it with further evidence (DIY meta-analysis) or carry out some other type of experiment to test said inference.
Here's a very brief primer: https://socialresearchmethods.net/kb/concval.php
Huge surveys long showed a correlation between smokers and increased incidence of cancer. But it took decades of research to rule out confounding factors. For example, it may have been the case that industrial work was causing the cancer and industrial workers just happened to be more likely to be smokers.
There is one tool that really nails causation fairly quickly which is a double blind controlled experiment. But usually in social science it's very hard to or even immoral to conduct such experiments.
For example, assigning babies at random to be smokers or not for some period would be pretty hard to carry out and certainly be immoral if you thought the smoking may lead to cancer.
They succeeded in corrupting the political process though. I don't think you should need 100% scientific certainty to begin regulatory action. Maybe 80% or 90% of the way is good enough. The industry succeeded in requiring 150% certainty before a public health response could begin.
It's standard for all data, but must be enforced more strictly in social sciences where controlling for all variables (let alone knowing what they all are!) is impossible, and your correlations are more likely to be pure chance.
See: reproducibility crisis in social sciences
Me expressing my opinion, but I don't like either. So I try to steer away from expressing "my opinion," and sticking to only things that have a high probability of being true, which in this case, is the GGGP using an imperfect study incorrectly.
Your data is not relevant to the hypothesis. The surveys were conducted in different time-frames (Canada: 2009-2014, USA 2005-2010), which I shouldn't need to tell anyone why that dataset is not useful for rigorous comparison, but I will anyway: one surveys people who were just recently hit by a recession and tracks them throughout the economy's recovery; while the other surveys people who recently experienced massive economic growth (exuberance) and tracks them throughout their economy crashing. Both, Canda's and the USA's, economies were intertwined during the 2007-2009 financial crisis, and this should be accounted for -- but it isn't.
Furthermore, the data DOES hold up to the GP's assertion: that as the unemployment rate goes up, the gap in life satisfaction between employed and unemployed approaches zero (chart 2). That is, 20% nationwide unemployment, unemployed and employed Canadians have the same life satisfaction (chart 1).
Lastly, this is social sciences. We cannot conclude anything from the data without experimentation. At best, this data is observational reason to pursue deeper inquiry, at worst it's idealogical fodder to drive other people's agends by lying with statistics. Everyone does it. Statistics don't really mean anything on their own, and lay-people give them too much weight!