Imagine a project where:

• The analysis consists of many factors.
• A high level of uncertainty exists about the outcome.
• Analysts or decision makers hold competing views.

How do you decide which analyst or opinion is right (or at least, more right)? Resist the urge to determine that your own (or your first) hypothesis should be the focus of your analysis by using the Multiple Hypothesis Generation structured analytic technique.

A hypothesis, in broadest terms, is a potential explanation or conclusion that is to be tested by collecting and presenting evidence. It is a declarative statement that has not been established as true – an “educated guess” based on observation to be supported or refuted by more observation or through experimentation.

Generate multiple hypotheses at the start of your project to avoid these common pitfalls:

• Coming to a premature closure.
• Being overly influenced by first impressions.
• Selecting the first answer that appears “good enough.”
• Focusing on a narrow range of alternatives that represent marginal, not radical, change.
• Opting for what elicits the most agreement or is desired by the boss.
• Selecting the alternative that avoids a previous error or replicates a past success.

A good hypothesis: is written as a statement (not a question); is based on observations and knowledge; is testable and falsifiable; predicts the anticipated results clearly; and contains a dependent and independent variable.

Here is a simple way to begin a Multiple Hypothesis Generation  exercise:

1. Gather a diverse group together to review the available evidence and explanations for a given issue, activity, or behavior.
2. Ask each member of the group to write down one to three alternative explanations or hypotheses.
3. Collect, consolidate, and display the results.
4. Employ group and individual brainstorming techniques to identify key forces and factors.
5. Aggregate the identified forces and factors into affinity groups, and label each group.
6. Use problem restatement and “considering the opposite” to develop new ideas.
7. Update the list of alternative hypotheses, striving to keep them mutually exclusive.
8. Select the most promising hypotheses.

Multiple Hypotheses Generator®

Globalytica’s Multiple Hypotheses Generator® can help analysts avoid traps and biases frequently faced at the start of the analytic process or when a hypothesis has become the “common wisdom.” This should be an integral part of any rigorous analytic process; it helps analysts avoid surprise if and when that common wisdom turns out to be wrong. The Multiple Hypotheses Generator® provides a structured way to generate a mutually exclusive set of hypotheses for explaining a particular issue, activity, or behavior. It decreases the likelihood of a key hypothesis being overlooked.

The Multiple Hypotheses Generator® works by using a permutation tree to create a set of hypotheses consisting of each possible combination of the analyst’s answers to the “Who”, “What”, “When”, “Where”, “Why”, and “How” questions. Here is a graphical representation of how the permutations are created in the Multiple Hypotheses Generator®.

A Multiple Hypothesis Generation exercise is possible to do by hand, but using the Multiple Hypotheses Generator® software makes this process more efficient because it creates and sorts the permutations for you.

Learn More > Globalytica’s Multiple Hypothesis Generator®.

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