Hypothesis

The concept of a hypothesis can be used in many different context, frameworks, and processes. The hypothesis-driven approach helps ensure that decisions and innovations are based on evidence (and continuous learning?), [cons]-> making it valuable in any field that requires informed, data-backed decisions.

Hypothesis-driven design decisions

The process focuses on starting with assumptions that can be tested, collecting data, and making informed iterations.

Hypothesis-driven design

1. Identify a problem or opportunity. Clear problem statement
2. Formulate a hypothesis.
3. Plan an experiment to test the hypothesis.
4. Collect Data and Feedback
5. Analyze Results.
6. Compare the results from the control and test groups against the hypothesis.
7. Outcome Evaluation.
8. The hypothesis is supported by data, indicating that the design decision had the intended (?) effect.
9. The data does not support the hypothesis, suggesting that the change did not yield the expected outcome.
10. Draw Conclusions and Iterate. Based on the analysis, decide the next steps:

• Refine the Design: Make adjustments based on insights.
• Pivot: If the hypothesis was disproved, consider alternative solutions.
• Implement Fully: If the hypothesis was validated, roll out the design more broadly.

McKinsey Flow There is a repeating cycle of forming and testing hypotheses. McKinsey consultants follow three steps in this cycle:

1. Form a hypothesis about the problem and determine the data needed to test the hypothesis
2. Gather and analyze the necessary data, comparing the result to the hypothesis
3. Update the model of the problem space and form a new hypothesis

Consultants are careful to form very specific hypotheses. This enables them to quickly determine the data they need to test their hypotheses. It also allows them to only collect the data they need to test their hypotheses. Consultants do not gather data to fish for an answer, they seek to test their hypotheses.

Testing a hypothesis. There are times when one needs a high degree of certainty in their answer. However, for most professionals in most situations, this is not the case. This creates a straightforward litmus test: Do you need a great deal of certainty in your answer? Or is a bias towards speed more valuable?

links: https://www.linkedin.com/pulse/what-i-learned-mckinsey-part-3-how-hypothesis-driven-devin-kasper/ https://en.wikipedia.org/wiki/Hypothesis

concepts

framer of a hypothesis data-backed decisions

to_sort

In common usage in the 21st century, a hypothesis refers to a provisional idea whose merit requires evaluation. For proper evaluation, the framer of a hypothesis needs to define specifics in operational terms. A hypothesis requires more work by the researcher in order to either confirm or disprove it.

In entrepreneurial setting, a hypothesis is used to formulate provisional ideas about the attributes of products or business models. The formulated hypothesis is then evaluated, where the hypothesis is proven to be either “true” or “false” through a verifiability- or falsifiability-oriented experiment. https://en.wikipedia.org/wiki/Hypothesis#cite_note-11 (https://en.wikipedia.org/wiki/Hypothesis#cite_note-12)