Precoil
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Precoil

Biases in Innovation and Entrepreneurship

Biases aren’t necessarily bad. They’ve helped us survive as a species, but they aren’t always beneficial when it comes to innovation and entrepreneurship.

Biases can unsuspectingly seep into our processes and lead us down a path of wasted time, effort and money.

There are several types of biases, but I repeatedly observe the same three major biases time and time again when working with anyone trying to create something new.

1. Confirmation Bias

Confirmation Bias

Confirmation bias is the tendency to interpret new evidence as confirmation of one’s existing beliefs or theories.

The risk is that you prematurely validate a hypothesis, then move on to wasting money building something more expensive.

It occurs when you set the bar too low in your experimentation, so that you always prove yourself right in the end.

Some tips you can use to address confirmation biases are to set the success criteria bar high, to help address your need to always be right. You can also create competing hypotheses to challenge your confirmation biases.

2. Overconfidence Bias

Overconfidence Bias

Overconfidence bias is the self perception that one’s judgement is reliably greater than objective accuracy.

The risk is that you have an excessive confidence in your own answers to questions. You believe your own hype.

It generally occurs when you focus on the ability to know the answer, no matter what the question and even downplay the need for experimentation at all.

You can address this in a couple different ways. One is to perform open ended discovery testing with directional hypotheses to explore more than one answer. Another is to test multiple ideas at the same time to determine which combination is the best.

3. Experimenter Bias

Experimenter Bias

Experimenter bias is the process where the scientists performing the research influence the results to portray a certain outcome.

The risk here is that you only believe data that agrees with your hypothesis.

It usually occurs when you discard data that conflicts with your hypothesis. This results in manipulating the results to give the illusion of being correct in your prediction.

You can help mitigate this from occurring by involving others in the data synthesis process to bring in different perspectives. You should also run multiple experiments to generate evidence for each hypothesis.

Can We Eliminate These Biases Entirely?

Short answer — No.

Longer answer — You can educate yourself and become more aware of these biases, which is a big first step. Even better, you can surround yourself with an open minded and diverse team which helps dampen the effects of these biases on your day to day work.

I will leave you with one of my favorite quotes in our Testing Business Ideas book from Paul Saffo.

Allow your intuition to guide you to a conclusion, no matter how imperfect — this is the “strong opinion” part. Then — and this is the “weakly held” part — prove yourself wrong.
— Paul Saffo

Interested in how to test your business ideas? Feel free to contact me.

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