{"id":569,"date":"2010-12-05T10:39:56","date_gmt":"2010-12-05T10:39:56","guid":{"rendered":"http:\/\/www.saasoft.com\/blog\/?p=569"},"modified":"2010-12-05T10:39:56","modified_gmt":"2010-12-05T10:39:56","slug":"comparison-or-improvement-that-is-the-question","status":"publish","type":"post","link":"https:\/\/hcse.blog\/?p=569","title":{"rendered":"Lies, Damned Lies and Statistics!"},"content":{"rendered":"

\"\"<\/a>Most people are confused by statistics and\u00a0because of this experts\u00a0often\u00a0regard them as\u00a0ignorant, stupid or both.\u00a0 However, those\u00a0who claim to be experts in statistics\u00a0need to proceed with caution\u00a0– and here is why.<\/p>\n

The\u00a0people who are confused by statistics\u00a0are confused for a reason – the statistics they see presented do not make sense to them in their world.\u00a0 They are\u00a0not stupid – many are\u00a0graduates and have high IQ’s\u00a0– so this means they must be ignorant and the obvious solution is to tell\u00a0them to go and learn\u00a0statistics.\u00a0This is the strategy adopted in medicine: Trainees are expected to\u00a0invest some time doing research and in the process they are expected to learn\u00a0how to use statistics in order to develop their critical thinking and decision making.\u00a0 So far so good, so what\u00a0 is the outcome?<\/p>\n

Well, we have been running this experiment for decades\u00a0now – there are millions of peer reviewed papers published – each one\u00a0having\u00a0passed the scrutiny of a\u00a0statistical expert – and yet we still have a health care system that is not delivering what we need at a cost we can afford.\u00a0 So, there must be someone else at fault – maybe the managers! They are not expected to learn or use statistics\u00a0so that statistically-ignorant rabble\u00a0must be\u00a0the problem -so the next plan is\u00a0“Beat up the managers”\u00a0and “Put statistically trained doctors in charge”.<\/p>\n

Hang on a minute! Before we nail the managers\u00a0and restructure the system let us step back and consider another more radical hypothesis. What if there\u00a0is something not right about the statistics\u00a0we are using?\u00a0The medical statistics experts\u00a0will rise\u00a0immediately and state\u00a0“Research statistics is a rigorous science derived from first principles and is mathematically robust!”\u00a0 They are correct.\u00a0It is.\u00a0But all mathematical derivations are based on some initial fundamental assumptions so when the output does not seem to work in all cases then it is always worth re-examining the\u00a0initial assumptions. That is the tried-and-tested path to new breakthroughs and new\u00a0understanding.<\/p>\n

The basic assumption that underlies\u00a0research statistics is that\u00a0all measurements are independent of each other<\/em> which also implies that order and time\u00a0can be ignored.\u00a0 This is\u00a0the reason that so much effort, time and money\u00a0is invested in the design of\u00a0a research\u00a0trial – to ensure that the statistical analysis will be\u00a0correct and the conclusions will be valid.\u00a0In other words\u00a0the research trial is designed around the statistical analysis method and its founding assumption. And that is OK when we are doing\u00a0research.<\/p>\n

However,\u00a0when we come to apply the output of our research trials to the Real World we have\u00a0a problem.<\/p>\n

How do we demonstrate that implementing the research recommendation\u00a0has resulted in an improvement? We are outside the\u00a0controlled environment of research now and\u00a0we cannot distort the Real World to suit our statistical paradigm.\u00a0 Are the statistical tools we used for the research still OK? Is\u00a0the founding assumption still\u00a0valid?\u00a0Can we still\u00a0ignore time? Our answer is clearly\u00a0“NO” because we are looking for a change over time! So can we assume the measurements are independent – again our answer is “NO” because for a process the measurement we make now is influenced by the system\u00a0before, and the same system will also influence the next measurement. The measurements are NOT independent of each other<\/strong>.<\/p>\n

Our statistical\u00a0paradigm suddenly falls apart because the founding assumption on which it is built is no longer valid.\u00a0We cannot\u00a0use the statistics that we used in the research when we\u00a0attempt to apply the output of the research to the Real World. We need a new and complementary statistical\u00a0approach.<\/p>\n

Fortunately for us it already exists\u00a0and\u00a0it is called improvement statistics<\/strong> and we use it all the time – unconsciously. No doctor would\u00a0manage the blood pressure of a patient on Ward A\u00a0 based on the average blood pressure of the patients on Ward B – it does not make sense and would not be safe.\u00a0\u00a0This\u00a0single flash of insight is enough to explain\u00a0our\u00a0confusion. There is more than one\u00a0type of statistics!<\/p>\n

New\u00a0insights\u00a0also offer new options and\u00a0new\u00a0actions. One\u00a0action would be that\u00a0the Academics\u00a0learn improvement statistics so that they\u00a0can understand better the world outside research;\u00a0another action would be that the Pragmatists learn\u00a0improvement statistics so that they can apply the output of well-conducted research in the Real World in a rational, robust\u00a0and safe\u00a0way. When both groups have a common language the\u00a0opportunities for systemic improvment increase.\u00a0<\/p>\n

BaseLine\u00a9 is\u00a0a tool designed specifically to offer\u00a0the novice a path\u00a0into\u00a0the world\u00a0of improvement statistics.<\/p>\n","protected":false},"excerpt":{"rendered":"

Most people are confused by statistics and\u00a0because of this experts\u00a0often\u00a0regard them as\u00a0ignorant, stupid or both.\u00a0 However, those\u00a0who claim to be experts in statistics\u00a0need to proceed with caution\u00a0– and here is why. The\u00a0people who are confused by statistics\u00a0are confused for a reason – the statistics they see presented do not make sense to them in their … <\/p>\n

Continue reading “Lies, Damned Lies and Statistics!”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6,7,17,22,24,25,34,35,43,45],"tags":[57,75,110,126,138,143,194,238,262,290,296],"class_list":["post-569","post","type-post","status-publish","format-standard","hentry","category-6m-design","category-baseline","category-examples","category-healthcare","category-improvementology","category-information","category-questions","category-reflections","category-why","category-what","tag-baseline","tag-confusion","tag-expert","tag-healthcare","tag-improvement","tag-innovation","tag-paradigm","tag-research","tag-statistics","tag-time","tag-trial"],"_links":{"self":[{"href":"https:\/\/hcse.blog\/index.php?rest_route=\/wp\/v2\/posts\/569","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hcse.blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hcse.blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hcse.blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/hcse.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=569"}],"version-history":[{"count":0,"href":"https:\/\/hcse.blog\/index.php?rest_route=\/wp\/v2\/posts\/569\/revisions"}],"wp:attachment":[{"href":"https:\/\/hcse.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=569"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hcse.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=569"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hcse.blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=569"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}