<\/p>\n
No, your eyes are not deceiving you; the title of the blog has changed slightly, from \u201cHow to Make Truly Terrible Graphs\u201d to \u201cHow to Make Truly Terrible Tables.\u201d This reflects the fact that it is possible to screw up (am I allowed to say that? Let\u2019s make it \u201chave things go amiss\u201d) in areas other than graph-making. So, in the next few blogs, we\u2019ll turn our attention to making tables for papers and presentations. (As a woodworker, I\u2019ve screwed up making other types of tables, but that discussion will have to wait for a different forum.) The second part of the title may also raise some eyebrows; how can you be too accurate? After all, the need for accuracy has been drummed into our heads since we were scientists-in-training, learning the rules of the game at our supervisor\u2019s knee. Whether we\u2019re using an extremely expensive piece of lab equipment or designing a new paper-and-pencil scale, the mantra is the same: reduce the error in order to improve the reliability of our measurements and increase the accuracy. So in presenting our results in a table, how can we be \u201ctoo accurate?\u201d<\/p>\n
As a matter of fact, it\u2019s actually quite easy; all we have to do is ignore the imprecision inherent in any measurement and just keep printing out all of those numbers to the right of the decimal point. For starters, let\u2019s take a look at Table 1, presenting some basic demographic information for a group in a study.<\/p>\n
<\/p>\n
Table 1<\/p>\n
Demographic Information<\/p>\n
| \n Variable<\/p>\n<\/td>\n | \n Group 1<\/p>\n<\/td>\n | \n Group 2<\/p>\n<\/td>\n<\/tr>\n |
| Number of males\/females<\/td>\n | \n 6\/4<\/p>\n<\/td>\n | \n 5\/5<\/p>\n<\/td>\n<\/tr>\n |
| Age in Years (SD)<\/td>\n | \n 38.25 (10.05)<\/p>\n<\/td>\n | \n 37.60 (9.90)<\/p>\n<\/td>\n<\/tr>\n |
| Education in Years (SD)<\/td>\n | \n 13.45 (4.20)<\/p>\n<\/td>\n | \n 12.90 (4.15)<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n Starting off with Age, we report that it\u2019s 38.25 years for the 10 people in Group 1. If we determined age by asking the people how old they were at their last birthday, then on average, there\u2019ll be an error of about 180 days. For example, at my last birthday, I was 73 years old, but I\u2019m actually 73 years, 8 months, and 12 days old on the day that I\u2019m writing this. (For those who want to send cards or presents, my actual birth date is 12 November; my mailing address is available on request.) We can improve the accuracy by asking people how old they are as of their nearest birthday, but that decreases the error to \u201conly\u201d 90 days, on average. Now, just what does that \u20185\u2019 in the second decimal place represent? It\u2019s 1\/100th<\/sup> of a year, or 3.65 days. Given the degree of inaccuracy in how we measured age to begin with, can we really justify this degree of accuracy in reporting the results, especially given that there are only 10 people in the group? If just one person in the group were replaced with another who is one year older, that would change the first<\/i> decimal place from 2 to 3, or slightly more than one month. To claim that we know an average participant\u2019s age to four days\u2019 accuracy does violence to the data.<\/p>\n In fact, that overestimation of the precision of the data pales in comparison to our estimate of the participants\u2019 education. Because the school year is about 200 days long (and they often seemed like very long days), then the last decimal place represents two days in class. Do you really think the data can support this degree of accuracy? I thought not.<\/p>\n If you think that these examples are fairly extreme, then (in the words of TV pitch men), \u201cBut wait \u2013 there\u2019s more!\u201d I just checked a Web site for the population of Brazil, and the number it reported was 206,769,143. Seriously? Even if that\u2019s based on some equation taking into account the estimated birth and death rates, let\u2019s examine where the numbers came from. There first had to be a census to establish the baseline, and that data-gathering was likely spread out over many weeks or months, covering not only major cities but also remote villages buried deep in the Amazonian forest. During that time, some people were dying and others being born. But let\u2019s not forget the words of Sir Josiah Stamp (1880-1941), a statistician and former Director of the Bank of England: \u201cThe government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the chowy dar<\/i> [village watchman in India], who just puts down what he damn pleases.\u201d Then, the birth rate is 14.46\/1,000 population, or slightly over 340 new souls per hour<\/i>! (The figure for deaths is about 154\/hour.) So, that final \u201c143\u201d in the population estimate is wrong within an hour of being written down. It would be far more \u201caccurate\u201d to say that the population is 206.8 million and leave it at that, indicating that the estimate is really just that \u2013 an estimate.<\/p>\n So remember, too much accuracy in a table is inaccurate.<\/p>\n <\/p>\n","protected":false},"excerpt":{"rendered":" How to Make Truly Terrible Tables: A Tutorial by David Streiner Part I: Be Too Accurate No, your eyes are not deceiving you; the title of the blog has changed slightly, from \u201cHow to Make Truly Terrible Graphs\u201d to \u201cHow to Make Truly Terrible Tables.\u201d This reflects the fact that it is possible to […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[12,4],"tags":[],"_links":{"self":[{"href":"https:\/\/labmath.org\/index.php?rest_route=\/wp\/v2\/posts\/495"}],"collection":[{"href":"https:\/\/labmath.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/labmath.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/labmath.org\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/labmath.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=495"}],"version-history":[{"count":6,"href":"https:\/\/labmath.org\/index.php?rest_route=\/wp\/v2\/posts\/495\/revisions"}],"predecessor-version":[{"id":501,"href":"https:\/\/labmath.org\/index.php?rest_route=\/wp\/v2\/posts\/495\/revisions\/501"}],"wp:attachment":[{"href":"https:\/\/labmath.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=495"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labmath.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=495"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labmath.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=495"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}} |