99% of all statistics are wrong


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99% of all statistics are wrong

  1. 1. 99% of AllStatistics AreWrongSo the story goes, 50% of all investments will never bring a return, in a relatedstory, 99% of all statistics are wrong. The thinking person asks “does that includethe statistic that says that 99% of all statistics are wrong as well?”Weighted DataPointsThe inherent risk of any statistical or mathematically engineered formula is thatyour results are inexorably tied to the quantification of simultaneous data pointswith the result of a derivative zero point analysis of digital integers. In other words,it’s made up! It’s made up based on what you have right now and it’s just a numberthat has no empirically testable meaning. This is true of any provider out there. Sowhat’s the sauce then?The SauceThe main ingredient of any tomato sauce is the tomato. Getting into the businessof canning and distributing tomato sauce with the intent of total re-invention isfoolish. When we compare the taste of one sauce against another, we should look
  2. 2. for the differences not similarities to enhance or change our sauce. Creating agreat sauce versus a good sauce can simply be by adding a few additionalingredients in the correct proportion. In many instances instead of just having a“good sauce” we will then have a “great sauce”. To follow this analogy is like thedifference between an “influencer formula” that functions as you would expectversus one that gives you the results you can actually do something with to bettermarket your service or product.Many experts agree that the value of your results is proportionally linked to theamount of data churned. More data equals better results, not necessarily correct.A few organizations try to correct this by adding human analysis to counter the badconclusions thus trying to statistically improve accuracy. Since computers canonly imply what you program them to imply, you will reach a negative conclusionquickly. So what conclusions are you left with?Shaping TheDataAccuracy may always be some-what relative, and that allows us to make certaindefinite conclusions. The ultimate goal of any Influencer Formula is to provideinformation as a deliverable to clients. In creating a discussion around developingthis framework from communities input, there are ulterior factors at play that are ofcritical importance. In my view, the defining factor should be delivering contextrelative to the nature of the clients’ needs. The reality seems to persist that even ifyou have a small amount of data you need to accurately define the context. If youare able to accomplish this it would make reaching a conclusion of sentiment asimpler part of the equation.Posted in: Influencer Formula