In this OkCupid blog post they argued that the distribution of messages "dramatically illustrates just how much more important a woman’s looks are than a guy’s".
I transcribed this graph from Dataclysm into numbers and computed the share of message that go to the top 20 percentiles.
import numpy as np m = np.array([0.313, 0.322, 0.33 , 0.338, 0.347, 0.355, 0.363, 0.37 , 0.376, 0.383, 0.389, 0.395, 0.402, 0.408, 0.417, 0.427, 0.437, 0.446, 0.456, 0.466, 0.476, 0.486, 0.496, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.506, 0.515, 0.526, 0.536, 0.547, 0.557, 0.568, 0.578, 0.589, 0.599, 0.61 , 0.62 , 0.631, 0.642, 0.652, 0.663, 0.673, 0.684, 0.694, 0.705, 0.715, 0.726, 0.736, 0.747, 0.757, 0.768, 0.778, 0.789, 0.801, 0.813, 0.825, 0.838, 0.85 , 0.862, 0.875, 0.887, 0.899, 0.912, 0.924, 0.939, 0.969, 1. , 1.03 , 1.06 , 1.091, 1.121, 1.151, 1.182, 1.212, 1.239, 1.265, 1.291, 1.318, 1.344, 1.37 , 1.396, 1.454, 1.555, 1.656, 1.757, 1.858, 2.019, 2.205, 2.459, 3.125, 4.236]) f = np.array([ 0.973, 1.106, 1.24 , 1.373, 1.506, 1.64 , 1.773, 1.906, 2.021, 2.111, 2.201, 2.291, 2.381, 2.471, 2.561, 2.651, 2.741, 2.826, 2.906, 2.986, 3.066, 3.145, 3.225, 3.305, 3.385, 3.465, 3.544, 3.63 , 3.732, 3.834, 3.935, 4.037, 4.139, 4.24 , 4.342, 4.443, 4.545, 4.647, 4.748, 4.841, 4.927, 5.013, 5.099, 5.185, 5.271, 5.356, 5.442, 5.528, 5.614, 5.7 , 5.786, 5.872, 5.958, 6.064, 6.178, 6.292, 6.406, 6.521, 6.635, 6.749, 6.863, 6.977, 7.091, 7.214, 7.358, 7.503, 7.647, 7.791, 7.935, 8.08 , 8.224, 8.368, 8.513, 8.675, 8.844, 9.013, 9.182, 9.352, 9.521, 9.69 , 9.859, 10.029, 10.203, 10.384, 10.566, 10.747, 10.928, 11.109, 11.291, 11.472, 11.827, 12.616, 13.405, 14.193, 14.982, 16.707, 18.432, 20.156, 22.818, 26.411]) m[80:].sum() / m.sum() # 0.4144903913161593 f[80:].sum() / f.sum() # 0.41054106074829816 m.mean() # 0.8466300000000001 f.mean() # 6.774839999999999 f.mean() / m.mean() # 8.002126076326139
So it is 41/20 for both sexes. No evidence that men preferentially target the most attractive members more than women at all!
We can also see that, on average, women receive 8 times as many messages as men, and even accounting for the slight surplus of males on OkCupid (54:46 M:F), it would still be (50/46) 0.85 = .92, (50/54) 6.77 = 6.27, so still 6.8 times as many messages.
I could create a similar graph to the one in the OkCupid blog by re-binning the data from Dataclysm with very weird bin edges [0, 1, 25, 50, 75, 95, 98, 100] and by dividing by the 1st percentile (dashed lines is the re-binned Dataclysm data):
https://i.imgur.com/jGwjwYm.png
It does not fit perfectly, so maybe they even used different bins for each sex.
So either…
- I'm misunderstanding something. (Not a scientist so that could be…)
- The graph in Datacylsm is wrong, or…
- The OkCupid blog was wrong. Maybe that's also why they deleted this article?
[–]MieSelph5 points6 points7 points (0 children) | Copy Link