Pavel Nakonechnyy

The HADI Cycle for Hypothesis Testing

Published by Pavel Nakonechnyy on in Business Analysis.

The HADI cycle is a framework used in business and problem-solving to generate and test hypotheses. The acronym HADI stands for Hypothesis, Action, Data, and Insight. This cycle helps organizations organize their test results and gain insights about their business activity.

In this article, you’ll learn the steps of HADI cycle, its benefits and considerations together with advice from my experience of using HADI as a Business Analyst.

Steps of the HADI Cycle

  1. Hypothesis: The first step in the HADI cycle is to generate a hypothesis. A hypothesis is an educated guess that can be tested through research and experimentation. It identifies the problem and proposes a potential cause or solution. For example, a hypothesis could be that the website’s checkout process is too complicated, causing customers to abandon their shopping carts. However, such a problem is too wide and vague. First, apply the Issue Tree method to decompose complex problems into testable factors and assumptions.
  2. Action: Once a hypothesis is formulated, the next step is to take action based on the hypothesis. This could involve making changes or implementing strategies to test the hypothesis. For example, if the hypothesis is that the checkout process is too complicated, the action could be to allow customers to quickly order a single item with only their name and phone number to make it more user-friendly.
  3. Data: After implementing the action, data is collected to measure the impact and gather relevant information. This data could include metrics, user feedback, or any other relevant data points. The data collected helps evaluate the effectiveness of the action taken and provides evidence to support or refute the hypothesis.
  4. Insight: The final step of the HADI cycle is to draw insights from the data collected. By analyzing the data, organizations can make informed decisions and gain a deeper understanding of the problem or situation. These insights can then be used to refine strategies, make improvements, and optimize outcomes.

Benefits and Considerations

  • The HADI cycle allows businesses to quickly test ideas and eliminate ineffective ones.
  • It enables organizations to adapt their strategies based on data-driven decision-making.
  • Multiple hypotheses can be tested within a single HADI cycle, as long as they have different performance indicators.
  • Make sure to formulate hypotheses that can be tested and to gather and analyze relevant data to make informed decisions.
  • While the HADI cycle is a popular framework for hypothesis testing, there are alternative methods that can be used depending on the specific context and requirements such as the PDCA (Plan-Do-Check-Act) cycle widely used in quality management and continuous improvement, A/B testing is a method commonly used in marketing and product development, and build-measure-learn feedback loop in Lean.

Common Pitfalls in HADI Testing

While HADI testing can be a valuable tool for hypothesis testing, there are some common pitfalls to be aware of. These pitfalls can affect the accuracy and effectiveness of the testing process. Here are a few common pitfalls to avoid:

  1. Inadequate hypothesis formulation: Poorly formulated hypotheses can lead to ineffective testing. It is crucial to clearly define the hypothesis and ensure it is specific, measurable, achievable, relevant, and time-bound (SMART). Vague or ambiguous hypotheses can result in inconclusive or misleading results.
  2. Overreliance on single metrics: Relying solely on a single metric can be misleading. It is necessary to consider multiple metrics and indicators to have a comprehensive understanding of the impact of the taken action. Single metrics may not capture the full picture and can lead to incomplete or biased insights.
  3. Ignoring external factors: Neglecting to account for external factors that may influence the results can lead to inaccurate conclusions. Take time to consider and control for external variables that could confound the results and affect the validity of the testing.
  4. Avoiding qualitative hypothesis testing methods: While the HADI cycle is often associated with quantitative data analysis, incorporating both qualitative and quantitative analysis methods can provide a more comprehensive understanding of the data. Qualitative research methods, such as interviews, observations, and focus groups, can be valuable for generating insights, exploring complex phenomena, and understanding the context and motivations behind certain behaviors or outcomes.
  5. Ignoring expert judgment and domain expertise. Domain experts can generate several valid hypotheses about the problem in a matter of minutes and 80% of them will come true. At the same time, an expert can also help interpret the results.

By being aware of these common pitfalls and taking steps to avoid them, organizations can enhance the effectiveness and reliability of their HADI testing process.

Conclusion

Overall, the HADI cycle provides a structured approach to hypothesis testing, allowing organizations to generate insights and make informed decisions based on data.

Further reading

 

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