Common Report Mistakes to Avoid¶
None of these are tool limitations - they're presentation and interpretation choices that quietly undermine an otherwise solid report. Most are easy to catch once you know to look for them.
Treating a Small Segment Like a Robust Finding¶
A cross-tab built on a handful of responses can look just as clean as one built on hundreds - the chart doesn't visually communicate its own uncertainty. See Reading Cross-Tab Results: What to Look for Beyond the Percentages and When Your Sample Size Is Too Small to Trust a Result before presenting a thin-sample result with full confidence.
Reporting an Average Without the Distribution¶
A single average rating or NPS score can hide real polarization underneath it - see Interpreting NPS and Scale Question Results. Pairing a summary number with its underlying distribution catches this before it misleads a reader.
Forgetting Multi-Select Percentages Don't Sum to 100%¶
Presenting a multi-select breakdown without explaining why the percentages add to more than 100% invites a confused stakeholder to think something's broken. See Reading Multi-Select Results: Why Percentages Don't Add to 100% for a quick explanation worth including if your audience isn't familiar with this.
Implying Statistical Significance That Isn't There¶
Language like "significantly higher" implies formal statistical testing that isn't part of how Opionate's tools work. Using more careful language - "notably higher in this sample" - is both more accurate and, if anyone pushes back, easier to defend.
Choosing a Chart Type That Looks Impressive Over One That's Clear¶
A waterfall or treemap chart can look more sophisticated than a plain bar chart, but if the underlying comparison would read more clearly as a bar chart, the fancier option is working against your reader, not for them. See Choosing the Right Widget for Your Report.
Building a Report Once and Never Revisiting It¶
Since Reporting widgets reflect live data (see Why Two Views of the Same Report Can Show Different Numbers), a report built early in data collection and then exported once can look stale by the time it's actually shared - especially if response volume grew substantially afterward. Check the report against current data before sharing or exporting it.
Sharing a Report Without Checking Access Settings¶
Before sending a public report link, double check whether it should have a password or login requirement - see Sharing a Report Publicly. A report meant for internal eyes only, shared as a fully open public link by mistake, is an easy error with real consequences.
Skipping the Non-Technical Read¶
A report that makes perfect sense to the person who built it can be genuinely confusing to someone seeing it cold. See Designing a Report for a Non-Technical Audience for how to catch this before you share.
FAQ¶
Is there a way to check a report for these mistakes automatically?
No automated check - these are judgment calls worth reviewing manually before sharing or exporting a report, especially one going to an external or executive audience.
Which of these mistakes matters most for an internal, informal report?
Sample size caution and avoiding overstated significance language matter regardless of audience - the presentation-polish items matter more as the audience becomes less familiar with the underlying data.
Should I always include a caveat about sample size in my reports?
For any segment or finding built on a genuinely small sample, yes - a brief caveat is cheap insurance against a reader overreading the result.
For the fuller picture of presenting to a broader audience, see Designing a Report for a Non-Technical Audience.