Flipping the Conversation: From Research Validation to Research Evaluation
Research Craft
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In less mature research organizations or among individuals with limited exposure to strong research practices, research is often seen as a tool for validation. The goal becomes proving that an idea is correct, justifying a decision, or aligning with business expectations. However, this narrow approach limits the potential of research to drive real innovation. Instead, what if we shifted our focus from validating research to evaluating it?
Validation assumes that research exists to prove a point, often supporting a pre-defined roadmap or business objective. Evaluation, on the other hand, acknowledges that research is a process—one that should be scrutinized, tested, and refined. This shift in mindset can lead to better products, stronger user experiences, and more resilient business decisions.
The Problem with Validation in Tech
When product teams and leadership prioritize validation, they create an environment that rewards confirmation over exploration. This can lead to several systemic issues:
Biased decision-making: Research is often used to confirm what stakeholders already believe, rather than uncovering new insights.
Superficial user understanding: If research is designed to validate a predetermined outcome, it may ignore deeper user needs and challenges.
Missed opportunities: A validation mindset discourages questioning and iteration, which can stifle innovation.
The Case for Evaluation in Product Development
Shifting to research evaluation means focusing on learning, not just proving. Instead of asking, “Does this research confirm what we want?” product teams should ask:
What does this research reveal about our users’ true needs and behaviors?
Are our assumptions accurate, or do they need to be revisited?
How might this information shape the product in unexpected ways?
Are we considering diverse perspectives and alternative solutions?
This approach encourages:
Openness to unexpected findings: Negative or surprising results become opportunities for iteration rather than roadblocks.
More rigorous research: Teams are incentivized to design studies that genuinely explore user behavior instead of proving assumptions.
Stronger product development: By prioritizing evaluation, companies create products that better serve real user needs rather than just confirming internal biases.
Academia vs. Tech: How Research Mindsets Differ
Academia has long wrestled with the tension between validation and evaluation, but the tech industry has additional pressures—tight deadlines, ROI expectations, and stakeholder buy-in. While academia often leans toward deep exploration and theory-building, tech research is typically fast-paced and driven by business goals.
To bridge the gap, tech teams can borrow best practices from academia, such as:
Publishing internal research transparently: Sharing findings, including failures, within an organization to foster learning.
Prioritizing replication: Testing assumptions multiple times to ensure reliability before making major product decisions.
Encouraging cross-functional critique: Involving diverse teams in research review to prevent tunnel vision.
Valuing exploration as much as validation: Creating space for exploratory research that uncovers new opportunities, rather than just confirming existing plans.
Creating a Stronger Research Culture in Tech
By moving from validation to evaluation, the tech industry can develop products that truly meet user needs, rather than just confirming business expectations. Instead of asking, "Does this research support our current strategy?" teams should ask, "What does this research teach us, and how can we adapt?"
Flipping the conversation isn’t just a shift in process—it’s a fundamental change in how tech companies approach innovation, ensuring that research drives better decisions, not just convenient ones.
Written with the help of my AI Writing Assistant ChatGPT. Prompt and refinement by me.
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