This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists (and other people) test hypotheses by conducting experiments . The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from a hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, ., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk.  Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls . For example, tests of medical treatments are commonly run as double-blind tests . Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos , are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Furthermore, failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, ., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. (See the Duhem–Quine thesis .) Experiments can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider , at the bottom of an ocean, on Mars (using one of the working rovers ), and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.
I have already written a post on what Gurley meant and intended when he wrote this essay on the lifetime value model. In my blog post https:///2016/10/14/a-half-dozen-more-things-ive-learned-from-bill-gurley-about-investing/ I discuss issues like the game on the field problem. A “growth at virtually any cost” mentality can be dangerous and deadly for a startup. There is no hard and fast formula that determines the right level of paid spending on growth. High customer acquisition cost can quickly become uneconomic. The benefits of hyper growth eventually start to reflect an S curve most notably when the benefits of a network effects start to decline. At a point an additional user of a system no longer generates the same benefit it did when the company was smaller and had fewer users. For example, an incremental new user when you have only thousands of customers is worth far more than when you have millions of customers. Growth is still important but hyper growth driven by an outsize paid customer acquisition cost is no longer financially supportable. The goal of a business at that point should be greater organic growth driven by the sticky and viral growth engines.