Since most startups fail, there's a large number of false positive observations

<p>In the process of tuning a business model idea, entrepreneurs go through a series of processes, that are broadly referred to as <em>experiments</em> (see: <a class="wikilink" href="/experiments_for_idea_validation/">experiments for idea validation</a>). The statistics show that the majority of startups fail during their initial years, which should indicate that those experiments have a large number of false positive observations. </p> <p>In order to reduce those false positives, it is possible to apply the scientific method a bit more strictly (see: <a class="wikilink" href="/literature/202201061619_treating_a_business_model_as_a_scientific_model/">literature/202201061619 Treating a business model as a scientific model</a>). False positives may be related to biases (<a class="wikilink" href="/confirmation_bias/">confirmation bias</a>, for example), or to poorly defined objectives and metrics to measure the success of an experiment. </p> <p>I have struggled with <a class="wikilink" href="/establishing_thresholds_for_idea_validation/">establishing thresholds for idea validation</a>. Perhaps being overly optimistic regarding the meaning of the results is a common treat of entrepreneurs. </p> <p>Tags: <a href="/tags/entrepreneurship-principles">#entrepreneurship-principles</a> <a href="/tags/idea-validation">#idea-validation</a></p>