99% of All

Statistics Are

Wrong

So the story goes, 50% of all investments will never bring a return, in a related

story, 99% of all statistics are wrong. The thinking person asks “does that include

the statistic that says that 99% of all statistics are wrong as well?”

Weighted Data Points

The inherent risk of any statistical or mathematically engineered formula is that

your results are inexorably tied to the quantification of simultaneous data points

with 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 number

that has no empirically testable meaning. This is true of any provider out there. So

what’s the sauce then?

The Sauce

The main ingredient of any tomato sauce is the tomato. Getting into the business

of canning and distributing tomato sauce with the intent of total re-invention is

foolish. When we compare the taste of one sauce against another, we should look

for the differences not similarities to enhance or change our sauce. Creating a

great sauce versus a good sauce can simply be by adding a few additional

ingredients 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 the

difference between an “influencer formula” that functions as you would expect

versus one that gives you the results you can actually do something with to better

market your service or product.

Many experts agree that the value of your results is proportionally linked to the

amount 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 bad

conclusions thus trying to statistically improve accuracy. Since computers can

only imply what you program them to imply, you will reach a negative conclusion

quickly. So what conclusions are you left with?

Shaping The Data

Accuracy may always be some-what relative, and that allows us to make certain

definite conclusions. The ultimate goal of any Influencer Formula is to provide

information as a deliverable to clients. In creating a discussion around developing

this framework from communities input, there are ulterior factors at play that are of

critical importance. In my view, the defining factor should be delivering context

relative to the nature of the clients’ needs. The reality seems to persist that even if

you have a small amount of data you need to accurately define the context. If you

are able to accomplish this it would make reaching a conclusion of sentiment a

simpler part of the equation.

Posted in: Influencer Formula