What are common pitfalls to avoid when prioritizing test ideas? (Select two.)

Question

Options

• AOverlooking test complexity
• BAligning tests with business KPIs
• CIgnoring statistical significance
• DRelying solely on qualitative data

• A
  10% (6)
• B
  7% (4)
• C
  83% (48)

Explanation

When prioritizing test ideas, two critical pitfalls are ignoring statistical significance (C) and relying solely on qualitative data (D). If you ignore statistical significance, you risk acting on results that are random noise rather than real effects, leading to flawed decisions. Relying only on qualitative data (user interviews, opinions) without quantitative validation means your prioritization lacks measurable, reproducible evidence - both together undermine the integrity of your testing program.

Why the distractors are wrong:

• A (Overlooking test complexity) is actually something you should consider - complexity affects resource planning and risk - so it's a valid concern, not a pitfall to list here.
• B (Aligning tests with business KPIs) is best practice, not a pitfall; you want your tests tied to KPIs to ensure business relevance.

Memory tip: Think "Stats + Data = Valid Tests." The two pitfalls both involve cutting corners on rigor - one statistical (C), one evidential (D). If your test prioritization skips either, you're guessing, not testing.

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