# Statistical Result vs. Economically Meaningful Result

Statistical significance refers to the use of a sample to carry out a statistical test meant to reveal any significant deviation from the stated null hypothesis. We then decide whether to reject or not to reject the null hypothesis.

Economic significance entails the statistical significance and the economic effect inherent in the decision made after data analysis and testing.

The need to separate the two arises because some statistical results may look significant on paper yet they are not economically meaningful. The difference from the hypothesized value may carry some statistical weight but lack economic feasibility. Obviously, this makes implementation of the results very unlikely. Perhaps an illustrative example can help you gain a deeper understanding of the two concepts.

### Example: Statistical Significance and Economic Feasibility

A well-established pharmaceutical company wishes to assess the effectiveness of a newly developed drug before commercialization. The company’s board of directors commissions a pilot test. The drug is administered to a few patients for whom non of the existing drugs have been prescribed. A statistical test follows and reveals a significant decrease in the average number of days taken before full recovery. The company considers the evidence a sufficient enough a premise for the conclusion that the new drug is more effective than the existing alternatives.

However, production of the new drug is significantly more expensive, compared to the existing ones, because of the scarcity of active ingredients. Furthermore, the company would have to engage in a year-long lobbying exercise to convince the Food and Drug Administration and the general public that the drug is indeed an improvement to the existing brands. At the end of the day, the management decides to delay the commercialization of the drug because of the attendant production and introduction costs.

Other factors that may affect the economic feasibility of statistical results include:

• tax: financial institutions generally avoid projects that may increase the tax payable. Shareholders always eye increasing returns on investment from one year to the next;
• risk: we may have a statistically significant project that is too risky. Those that require the reservation of huge funds in the long term are particularly very risky. In fact, the additional risk is excluded from statistical tests.

Evidence of returns based solely on statistical analysis may not be enough to guarantee project implementation. In particular, large samples may produce results that have high statistical significance but very low applicability.

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