{"id":683,"date":"2008-04-09T10:00:51","date_gmt":"2008-04-09T17:00:51","guid":{"rendered":"https:\/\/www.scotthyoung.com\/blog\/2008\/04\/09\/don%e2%80%99t-analyze-the-anecdotes-when-you-need-data\/"},"modified":"2018-04-03T08:47:04","modified_gmt":"2018-04-03T15:47:04","slug":"dont-analyze-the-anecdotes-when-you-need-data","status":"publish","type":"post","link":"https:\/\/www.scotthyoung.com\/blog\/2008\/04\/09\/dont-analyze-the-anecdotes-when-you-need-data\/","title":{"rendered":"Don\u2019t Analyze The Anecdotes When You Need Data"},"content":{"rendered":"

\"Data.png\"<\/p>\n

In statistics, one of the first things you learn is the importance of n<\/em><\/strong>. The variable n<\/em><\/strong>, represents the size of the sample you\u2019re studying. And the size of n has a huge impact on whether your results are significant or just a fluke. Unfortunately it\u2019s easy to forget this lesson outside the classroom and spend too much time thinking about events with a sample size of 1.<\/p>\n

This kind of broken overanalysis can happen when you screw up. You get up to deliver a speech and bore your audience to tears. Doesn\u2019t it make sense to look into what mistakes you might have made, in order to fix the problem in the future? If you\u2019ve only given one speech, it doesn\u2019t. Comedians often practice a routine dozens of times to get the timing right, so it doesn\u2019t make sense to draw definite conclusions after one failure.<\/p>\n

Overthinking can also happen when you make a big win. When I started blogging, I was thrilled if one of my articles was picked up by a big website or received a huge traffic burst. The adrenaline rush of seeing your website stats make a right angle as they rocket upwards is an incredible feeling.<\/p>\n

My initial mistake, however, was after I\u2019d get a big win\u2013I\u2019d try to replicate that win with similar posts. These posts rarely worked because the first post had succeeded on luck. Trying to copy that success isn\u2019t possible, and I probably would have been better off writing something fresh.<\/p>\n

Why Analyzing Individual Cases Hurts You<\/h2>\n

There are a couple reasons looking too much into small samples hurts you. Here are just a few:<\/p>\n

1) Hesitation Replaces Action<\/h3>\n

Don\u2019t stop and think when you need to act quickly. When failing is fast and cheap, it doesn\u2019t make sense to stop and ponder over a single event. With blog writing, it takes just over an hour to test a new post idea. Why spend time analyzing when I could be out getting actual experiences?<\/p>\n

2) Superstition Replaces Trends<\/h3>\n

When you focus on individual events, you form false beliefs. I know people who swear that pigeons have targeting mechanisms when relieving themselves midair. They remember the one time bird droppings landed on their head, but can\u2019t see the thousands of times it landed safely to the ground. If possible, it\u2019s better to look at the overall trend than individual anecdotes.<\/p>\n

3) Gimmicks Replace Strategy<\/h3>\n

You can\u2019t analyze a strategy when you only look at the specifics. Some strategies will cause a lot of failures and a lot of wins at the same time. Babe Ruth was famous for having both the home run and strikeout record at the same time. But if you get caught up on a single event, you might switch to a strategy that avoids failures, but also avoids any wins.<\/p>\n

How to Ignore the Events and Focus on Trends<\/h2>\n

Humans are built to reason by stories, not statistics. I\u2019ll be more motivated to act after hearing an emotional plea than a set of pie charts. So it isn\u2019t easy to focus on the trends. I often succumb to anecdote analysis, even if I try to focus on the numbers.<\/p>\n

There are a couple ways I\u2019ve found useful to stay focused on the data:<\/p>\n

Measure Everything<\/h2>\n

You need to make the data louder than the numbers. One way to do that is to keep accurate measurements you review regularly. Taking measurements allows you to look back into the past, so you can see a broader trend over months and years, instead of days and weeks.<\/p>\n

Use Testable Hypothesis<\/h2>\n

Looking into an individual scenario can be useful for coming up with a hypothesis. Based on a few speeches, you might hypothesize that speaking more slowly would improve your success. That hypothesis is useful if you can test it in an experiment. Wait until you\u2019ve run a full experiment before you start drawing conclusions.<\/p>\n

Don\u2019t Give Yourself Time to Think<\/h2>\n

When the costs of failure are low, your immediate response to a mistake should be to get back up an try again. Don\u2019t give yourself a long delay in-between your attempts. Writing blog articles, giving speeches, exercise routines, dating and changing habits can all work through a fast, trial and error approach.<\/p>\n

What About When You Can\u2019t Fail Fast?<\/h2>\n

If you\u2019re in a business where working on the next product can take a year or two, failing fast isn\u2019t always an option. In these cases, it can make sense to look into anecdotes. But even with long project cycles, you can still improve the way you review.<\/p>\n

A good way is to take on \u201clearning projects\u201d. Learning projects are a typical project, except they test 2-3 hypothesis you\u2019d like to measure. When I finished my last e-book<\/a>, I wanted to test out subcontracting, working with AdWords and managing an affiliate system. I hadn\u2019t done any of those before, so taking on a large project would teach me something valuable about them.<\/p>\n

Another hint for analyzing long projects is to limit your review. Try to generalize any conclusions into hypothesis you can test in your next round. Creating specific conclusions that require a large sample to test aren\u2019t useful. If your first research report gets a C-, you might want to draw general hypothesis like, \u201cdo more resesarch\u201d or \u201cspend more time on proofreading\u201d rather than specific conclusions like \u201cI should have reworded paragraph 6.\u201d<\/p>\n

The real question about whether you should spend more time thinking or doing has to do with the cost of failure. If you can fail quickly without going broke, action beats analysis. If you have to fail slowly, be smart with your review so you don\u2019t start seeing trends that aren\u2019t there.<\/p>\n","protected":false},"excerpt":{"rendered":"

In statistics, one of the first things you learn is the importance of n. The variable n, represents the size of the sample you\u2019re studying. And the size of n has a huge impact on whether your results are significant or just a fluke. Unfortunately it\u2019s easy to forget this lesson outside the classroom and […]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[657,682],"tags":[29,25],"class_list":{"0":"post-683","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-creativity","7":"category-nc-learning","8":"tag-learning","9":"tag-motivation","10":"entry"},"acf":[],"yoast_head":"\nDon\u2019t Analyze The Anecdotes When You Need Data - Scott H Young<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.scotthyoung.com\/blog\/2008\/04\/09\/dont-analyze-the-anecdotes-when-you-need-data\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Don\u2019t Analyze The Anecdotes When You Need Data - Scott H Young\" \/>\n<meta property=\"og:description\" content=\"In statistics, one of the first things you learn is the importance of n. 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