payload: {"action":"closed","issue":{"url":"https://api.github.com/repos/pretalx/pretalx/issues/656","repository_url":"https://api.github.com/repos/pretalx/pretalx","labels_url":"https://api.github.com/repos/pretalx/pretalx/issues/656/labels{/name}","comments_url":"https://api.github.com/repos/pretalx/pretalx/issues/656/comments","events_url":"https://api.github.com/repos/pretalx/pretalx/issues/656/events","html_url":"https://github.com/pretalx/pretalx/issues/656","id":432985486,"node_id":"MDU6SXNzdWU0MzI5ODU0ODY=","number":656,"title":"Report Median Review Score","user":{"login":"oxinabox","id":5127634,"node_id":"MDQ6VXNlcjUxMjc2MzQ=","avatar_url":"https://avatars0.githubusercontent.com/u/5127634?v=4","gravatar_id":"","url":"https://api.github.com/users/oxinabox","html_url":"https://github.com/oxinabox","followers_url":"https://api.github.com/users/oxinabox/followers","following_url":"https://api.github.com/users/oxinabox/following{/other_user}","gists_url":"https://api.github.com/users/oxinabox/gists{/gist_id}","starred_url":"https://api.github.com/users/oxinabox/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/oxinabox/subscriptions","organizations_url":"https://api.github.com/users/oxinabox/orgs","repos_url":"https://api.github.com/users/oxinabox/repos","events_url":"https://api.github.com/users/oxinabox/events{/privacy}","received_events_url":"https://api.github.com/users/oxinabox/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":11,"created_at":"2019-04-14T14:36:33Z","updated_at":"2019-04-21T15:24:07Z","closed_at":"2019-04-21T15:24:07Z","author_association":"NONE","body":"## Problem you are facing\r\nMean is a poor metric for summarising scores from multiple sources.\r\nParticularly with small numbers of reviewers.\r\nSince the mean  allows a single reviewer who either hates or loves something\r\nto completely swing the score.\r\n\r\n## Possible Solution\r\nA better metric is median.\r\nAn alternative is _mean, dropping best and worst_.\r\n\r\n## Context\r\nI was looking at what talks to accept for our conference.\r\nOur nominal cutoff is a score of 4.\r\nI was double checking those that were boarderline.\r\nThe first one checked was scored `5, 4, 4, 4, 2`, so net score of 3.8\r\nI'ld rather be ignoring that 2 and 5 and be seeing it summarised as 4.\r\nThen when sorting it would have appears in the area of \"definately accept\",\r\nnot \"boarderline\""}}
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