Question 1

Which decisions benefit most from shop-alongs or in-store observation?

Accepted Answer

Shop-alongs and in-store observation are most useful for decisions that hinge on real-world shopper behavior — especially things like store layout, shelf placement, packaging design, and point-of-sale marketing. For example, if a product is underperforming, observing shoppers in the aisle can reveal whether the item is hard to find, poorly merchandised, or losing attention to a competitor's display — insights that surveys or sales data alone might miss. These methods excel at uncovering the "why" behind shopping choices, informing decisions on everything from signage and store navigation to assortment mix and in-store promotions. In short, any decision aimed at improving the in-store customer experience or shopper journey benefits from the firsthand perspective that shop-alongs provide.

Question 2

What audit practices satisfy governance requirements for AI-assisted qual?

Accepted Answer

When you use AI in qualitative research, set up processes that leave an audit trail and ensure human oversight, so you meet governance standards. First, document everything: what data you fed the AI (e.g., interview transcripts), which tool or model was used, and what results it produced (summaries, themes, etc.). This way, an auditor or team lead can later trace how conclusions were reached. Second, keep humans in the loop: have researchers or auditors review a sample of AI-generated outputs against the original qualitative data to verify accuracy and check for any bias or misinterpretation. If the AI is helping code responses or find themes, double-check that it isn't systematically missing a certain theme or skewing one way. Third, protect privacy: strip out or anonymize personal identifiers in the text before analyzing it with AI, and use approved secure tools so sensitive data isn't exposed. By combining thorough record-keeping, regular validation checks, and strict privacy safeguards, you create a governance-friendly framework for AI-assisted qual.

Question 3

How can research improve representation of people with disabilities?

Accepted Answer

Research can better represent people with disabilities by being deliberately inclusive at every stage - from recruiting participants, to designing research materials, to analysis. Start with recruitment: actively seek out individuals with a variety of disabilities (physical, sensory, cognitive) through partnerships with disability organizations or panels that include those voices, rather than hoping they appear in a general sample. Next, ensure the research itself is accessible: surveys should be screen-reader friendly and avoid tiny fonts or color-dependent cues; focus group facilities or interview setups should accommodate mobility aids, offer sign language interpretation or closed captioning, and allow caregivers if needed. Even question wording matters - use respectful, person-first language and avoid assumptions about abilities. You can also involve people with disabilities in the research design (e.g., as co-creators or advisors) to catch biases or barriers the team might overlook. By proactively designing for inclusion like this, you not only get more representative data but also signal that those consumer perspectives are valued - often leading to insights that drive innovation for all users.

Question 4

Do experimental designs always assume more sample is always better?

Accepted Answer

No — good experimental design is about the right sample size, not simply the biggest sample size. While having more respondents generally increases statistical power (making it easier to detect a true effect), it doesn't mean "more is always better" in every situation. Once you have enough sample to reliably detect the differences or effects you care about (determined by a power analysis), piling on more data yields diminishing returns and can even be counterproductive. Extremely large samples might flag tiny differences as "significant" even if they're not practically meaningful, which can be misleading. There's also the reality of cost and time: testing on 10,000 people when 1,000 would have given a clear answer is inefficient and could divert resources from other tests. In some cases — like medical trials or any test involving inconvenience to participants — ethics dictate using the minimum sample necessary to achieve valid results, to avoid exposing more people than needed. So, an experiment should recruit enough participants to be robust and credible, but not so many that you're just inflating numbers. The goal is to determine an optimal sample size based on expected effect size and confidence levels, rather than assuming that a bigger N inherently makes for a better study.

Question 5

Do focus groups inevitably result in groupthink in research?

Accepted Answer

No, focus groups do not inevitably lead to groupthink, but without skilled moderation there is a risk that dominant opinions can sway others. Groupthink happens when participants feel pressured (even subtly) to conform, and it can suppress honest, diverse feedback. However, a good moderator can prevent this by actively encouraging all members to share their individual views, especially quieter participants who might otherwise defer to louder voices. Techniques like asking people to write down ideas independently before discussing, or using round-robin speaking turns, help ensure a range of opinions get aired. In well-run focus groups, participants often disagree and offer contrasting perspectives, which is the opposite of groupthink. The key is creating a safe environment where differing opinions are welcome — when done right, focus groups yield rich insights rather than one-note consensus.

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