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What are the best practices for designing and testing data-driven hypotheses?

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Data-driven hypotheses are essential for effective decision support, as they help you identify and validate the most relevant and impactful factors for your problem or goal. However, designing and testing data-driven hypotheses is not a simple or straightforward process. It requires careful planning, execution, and evaluation to ensure that your hypotheses are sound, reliable, and actionable. In this article, we will discuss some of the best practices for designing and testing data-driven hypotheses, such as:

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Joey Lin CA CFA ACMA

Commercial CFO | M&A | Transformation | Top Linkedin Voice |

1 Define your question and scope

Before you start generating hypotheses, you need to have a clear and specific question that you want to answer with data. This will help you narrow down your focus, avoid irrelevant or misleading data sources, and set the criteria for success. You also need to define the scope of your analysis, such as the time period, the population, the variables, and the assumptions that you will use.

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Joey Lin CA CFA ACMA

Commercial CFO | M&A | Transformation | Top Linkedin Voice |
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In the business realm, my go-to method for crafting hypotheses is through collaboration with management. We gather insights from a treasure trove of resources: internal data, industry benchmarks, and our carefully selected group of peers (the competition, of course). With this trifecta of information at our fingertips, we're armed and ready to tackle any challenge.

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2 Generate multiple hypotheses

Once you have your question and scope, you can start brainstorming possible hypotheses that could explain or predict the outcome that you are interested in. You can use various methods to generate hypotheses, such as literature review, expert input, data exploration, or logical reasoning. The key is to generate multiple hypotheses that cover different angles and perspectives, and that are testable, falsifiable, and measurable.

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Joey Lin CA CFA ACMA

Commercial CFO | M&A | Transformation | Top Linkedin Voice |
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In the wacky world of business, I team up with management to cook up some original hypotheses – think of us as the mad scientists of strategy! Then, I unleash the brainstorming frenzy with the rest of the gang, because two heads are better than one, right? And if all else fails, I dive into the depths of the internet for some extra inspiration – because let's face it, Google knows everything

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3 Prioritize and select hypotheses

You cannot test all your hypotheses at once, so you need to prioritize and select the ones that are most promising and feasible. You can use various criteria to prioritize and select hypotheses, such as the expected impact, the availability and quality of data, the complexity and cost of testing, or the alignment with your objectives and constraints. You should also consider the risks and uncertainties associated with each hypothesis, and how they might affect your decision making.

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Deborah Ribeiro

Founder & CEO at Zayon | Pioneering Decision Intelligence in Brazil with 100% proprietary AI | Specialist in AI and finance |Empowering data-driven decisions for business growth
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Hypothesis prioritization is a critical step in data-driven experimentation. Given the multitude of potential hypotheses, effectively selecting which ones to test first is essential for maximizing resources and accelerating insights. This process involves ranking them based on various factors, such as potential impact, data availability, feasibility and alignment with overall business objectives. Assigning scores or weights to each hypothesis can facilitate a quantitative comparison, enabling data-driven decision-making. It is crucial to consider not only the upside potential, but also the associated risks. A comprehensive approach balances optimism with a realistic assessment of the challenges.

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4 Design and execute tests

After you have selected your hypotheses, you need to design and execute tests that will provide evidence to support or reject them. You can use various types of tests, such as experiments, surveys, simulations, or statistical analysis, depending on your data and hypotheses. You should also ensure that your tests are valid, reliable, and ethical, and that you follow the best practices for data collection, processing, and analysis.

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5 Evaluate and interpret results

Once you have completed your tests, you need to evaluate and interpret the results to draw conclusions and recommendations. You should compare the results with your expectations, assumptions, and criteria, and identify any discrepancies, anomalies, or biases that might affect your interpretation. You should also assess the strength, significance, and validity of your evidence, and how it relates to your question and hypotheses.

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6 Communicate and act on findings

The final step is to communicate and act on your findings, by presenting them to your stakeholders, explaining your methods, results, and implications, and recommending the best course of action based on your data-driven hypotheses. You should also solicit feedback, address any questions or concerns, and monitor and evaluate the outcomes of your decision. You should also document and share your process, findings, and lessons learned, and update or refine your hypotheses as needed.

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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?

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What are the best practices for designing and testing data-driven hypotheses?

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1 Define your question and scope

2 Generate multiple hypotheses

3 Prioritize and select hypotheses

4 Design and execute tests

5 Evaluate and interpret results

6 Communicate and act on findings

7 Here’s what else to consider

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