How can you manage uncertainty when testing a hypothesis?

2 Prioritize your tests

You cannot test everything at once, so you need to prioritize your tests based on the importance and uncertainty of your assumptions. A simple way to do this is to use a matrix that plots the impact and confidence of each assumption. The impact is how much your hypothesis depends on the assumption, and the confidence is how certain you are that the assumption is true. The assumptions that have high impact and low confidence are the ones that you should test first, as they are the most critical and uncertain.

3 Choose your methods

Depending on your hypothesis and your assumptions, you can choose different methods to test them. Some common methods are experiments, surveys, interviews, data analysis, benchmarking, and prototyping. Each method has its own advantages and disadvantages, such as cost, time, validity, reliability, and generalizability. You should consider these factors when selecting your methods, and also balance the trade-off between depth and breadth. Depth means going deep into a specific aspect of your hypothesis, while breadth means covering a wider range of aspects.

4 Plan your execution

Once you have chosen your methods, you need to plan how to execute them. This involves defining your objectives, scope, sample, variables, metrics, and procedures for each test. You also need to anticipate the possible challenges and limitations that you might face, such as ethical issues, data quality, access, bias, and errors. You should prepare contingency plans and mitigation strategies for these scenarios, and also define your criteria for success and failure. Planning your execution will help you reduce uncertainty and increase efficiency.

5 Analyze your results

After you have executed your tests, you need to analyze your results and draw conclusions. You should compare your results with your expectations, and see if they confirm or reject your hypothesis and your assumptions. You should also look for patterns, trends, outliers, and anomalies in your data, and try to explain them with logic and evidence. You should avoid jumping to conclusions or cherry-picking data that supports your hypothesis, and instead be objective and critical. Analyzing your results will help you validate or invalidate your hypothesis and your assumptions.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?