What's your best way to use domain knowledge and expert opinions in hypothesis testing?

The failures we experienced of course! Ask us Gen Xers! Our failures prove opportunities of the future. Think about the What If along with What is and What has to be…

With domain knowledge, you can spot opportunities and threats from a mile away. You know your audience inside and out, understanding their needs, preferences, and pain points. It's like being fluent in the language of your customers.

Much like the article's analogy of customer satisfaction, where understanding psychology and behavior is key, my expertise allows me to tailor strategies that resonate with stakeholders and organizational goals. Moreover, complementing domain knowledge, I strongly believe in the power of intuition. By marrying data-driven insights with intuitive thinking, I've unlocked innovative approaches that transcend the ordinary, enabling me to navigate intricate challenges and seize unique opportunities. The amalgamation of domain knowledge and intuition, as outlined in the article, underscores the essential toolkit for professionals aiming to excel in their respective domains.

Domain knowledge comes in two forms: that from our successes, and that from our failures...and they're different! Domain knowledge hopefully brings upside, but it should also provide downside protection, and that only comes from "unsuccessful experiments." Without both, it's incomplete.

Domain knowledge is your entrepreneurial edge—it's the nuance behind your numbers, the context within your KPIs. In startup landscapes often dictated by data, deep industry understanding allows you to ask the right questions and apply the most effective analytical tools. It turns data into actionable wisdom.

Please never forget that each level of stakeholder has a subject matter expert (SME) that can and should be engaged for perspective and insights. Often a primary contact will defer to their SME if additional details or questions arise. Also remember that the end user and first line of support are also valuable goldmines of practical feedback, because that's where the conversations are at their most unfiltered.

When testing a hypothesis, I see it like trying to solve a puzzle. You want to make sure you're asking the right questions, and that's where knowledge of the field (domain knowledge) comes in handy....more like a roadmap. Now, imagine you have friends who are experts in the area. I'd speak with them to get their thoughts and insights. During such engagements they would sometimes point out things I hadn't considered or help me to see the bigger picture. With these insights (from my domain knowledge and expert opinions) I can then carry out a test, and look at the results. If the outcome is not in line with expectations, I can go back to my expert friends/colleagues or reference my knowledge of the field to figure it out.

Expert opinions are fact patterns which often don't apply to all environments. Organizations need to use these as inputs in a larger process which incorporates contextual items (e.g., goals, resources, timeline, etc.) to derive paramount value.

Expert opinions are the X-factor in a startup's decision-making equation. Their insights can shine light on blind spots or validate your hypotheses, offering you a richer, 360-degree view of a challenge. Ignoring them is leaving valuable intellectual capital on the table.

Incorporating domain knowledge and expert opinions isn't just additive; it's multiplicative. Start with a laser-focused question. Use your own expertise to draft initial hypotheses, then enrich them with experts' insights. These combined perspectives strengthen your tests and add nuance to your conclusions/strategy.

You take your domain knowledge, your understanding of the market, and you combine it with the insights of these experts. You're like a detective piecing together a puzzle. You ask yourself, "Does this idea make sense in our industry? Will it resonate with our audience? What do the experts say?"

Identifying and framing a focused problem statement is critical. The key is being specific. Avoid broad problems like "How can we improve sales?" This can be done by listening to user needs to identify common pain points. Turn these into "How might we...?" or "What might be causing...?" questions to generate hypothesis ideas. Drill down to concrete issues affecting a defined part of the customer experience, operations, or a business metric. A focused problem statement guides hypothesis generation and enables constructing measurable tests. Well-framed problems are critical for leveraging hypothesis testing to drive targeted business results.

It's a delicate dance between what you know and what they know. You leverage their experience to refine your hypothesis. You test it, gather data, and analyze the results. And here's the kicker: You're not married to your hypothesis. If the data tells a different story, you adjust course.

Harnessing both domain knowledge (your "north star") and expert input (your "compass") supercharges your hypothesis testing. It not only elevates the precision of your conclusions but also accelerates the learning curve, saving you valuable time and resources while boosting your decision reliability.

Domain knowledge is essential for selecting the appropriate methodology and should be tailored to fit your specific problem statement (hypothesis). It guides your research so that your results are applicable to your industry/context. Expert opinion enhances hypothesis generation by borrowing methodologies from different knowledge domains to frame your problem statement in a novel light. This produces new insights into common problem statements, leading to ideation, innovation and creative solutions. Expert opinions can also broaden and deepen the knowledge domain in which your problem statement sits. This improves your understanding of how your variables (data) interact and relate, as well as its limitations.

To avoid bias, focus experts on identifying problems and needs, not solutions. Supplement qualitative opinions with quantitative data to test assumptions. Frame hypotheses neutrally, without projecting preferences. Validate ideas through controlled experiments, not just intuition. Maintain transparency in synthesizing disparate expert input. Creating a culture of openness and healthy skepticism will enable harnessing experience while mitigating bias. Stay grounded in the customer perspective and let objective data guide decisions. With vigilance, you can leverage expertise while letting hypothesis testing lead the way.

The experimental design (methodology) you use to test a hypothesis is critical to the validity of your results. This is because the methodology determines how the data is collected and analysed, which can impact the findings of your study. It is therefore essential to critically evaluate both your own domain knowledge and others’ expert opinions. This will help you include only relevant information applicable to the hypothesis you are testing, allowing you to draw more robust conclusions.

This article should include real-world examples or case studies showcasing how domain knowledge and expert opinions were successfully used in hypothesis testing. Concrete examples can make the concepts more relatable and understandable for readers.

Domain knowledge might rapidly be consumed by Generative AI processes in the coming era. Expert opinion data is valuable and being ingested now, meaning it might become more commoditized.