International Day of Women
March 8 2017 was another day of gathering forces to focus attention on social needs. As described on the internationalwomensday.com website, "International Women's Day celebrates the social, economic, cultural and political achievement of women. Yet progress has slowed in many places across the world, so global action is needed to accelerate gender parity. In 2016 leaders across the world pledged to take action as champions of gender parity - not only for International Women's Day, but for every day. Groups and individuals also pledged their support."
"In 2017, if leaders are seeking to declare their bold action via the IWD website and show their support for women's advancement, please see details."
Though this is a realtively new repeating action, we decided to take a look. It has had lots of publicity especially because of the current administration in Washington, and as a kind of follow on to the powerful gathering on Jan 21, 2017.
I received a note "wondering if there was any change on your data during the Barcelona vs Paris Saint-Germain football match, yesterday march 8 that took place in Barcelona. I mentioned this since the match was very "mind oriented" could I say, since it was impossible for Barcelona to win. if you read about the events that took place, you will realize that it is an event worth of being analysed due to the incredible events and the amount of attention it generated."
We can't make much of single events, and can't disentangle effects of two major world class social gatherings, but I will mark the beginning of the match (19:45 UTC) on the graph below. I should note also that because they are characterized by feelings of intense competition, sports events tend not to yield strong deviations according to GCP predictions.
Specific Hypothesis and Results
The GCP event was set for the full 24 hour day, as us usual. The result is Chisquare 86635.095 on 86400 df, for p = 0.285 and Z = 0.567. The beginning of the Barcelona-Paris Football match is marked.
The following graph is a visual display of the statistical result. It shows the second-by-second accumulation of small deviations of the data from what’s expected. Our prediction is that deviations will tend to be positive, and if this is so, the jagged line will tend to go upward. If the endpoint is positive, this is evidence for the general hypothesis and adds to the bottom line. If the endpoint is outside the smooth curve showing 0.05 probability, the deviation is nominally significant. If the trend of the cumulative deviation is downward, this is evidence against the hypothesis, and is subtracted from the bottom line. For more detail on how to interpret the results, see The Science and related pages, as well as the standard caveat below.
It is important to keep in mind that we have only a tiny statistical effect, so that it is always hard to distinguish signal from noise. This means that every
success might be largely driven by chance, and every
null might include a real signal overwhelmed by noise. In the long run, a real effect can be identified only by patiently accumulating replications of similar analyses.