Start from the question you set out to answer
Before you touch a single chart, write down the decision the survey was meant to inform. A survey about a product launch answers a different question than one about workplace morale, and the same data can look encouraging or worrying depending on what you are trying to learn.
Let that question decide which results matter. It keeps you from cherry-picking the numbers that look good and from getting lost in secondary metrics that have nothing to do with the choice in front of you.
Report the base (n) before any percentage
Every percentage rests on a base: the number of people who actually answered that question. '80% agree' means one thing out of 500 responses and almost nothing out of 5. Always show the n next to the percentage so anyone reading it can judge how much weight it carries.
Treat small bases as directional. A handful of answers can hint at a pattern worth exploring, but it is not evidence you can act on with confidence. Wait for more responses, or frame the finding as a signal rather than a conclusion.
It's not just good practice: AAPOR's code of ethics requires publishing the sample size alongside results and providing, on request, the unweighted base each subgroup estimate rests on.
Read the distribution, not just the average
An average hides the shape of the answers. A satisfaction score of 3 out of 5 can come from a room full of lukewarm 3s, or from an even split of delighted 5s and angry 1s. Those two situations call for opposite responses, yet they share the same mean.
Look at the full spread: how many people chose each option, where the peaks are, and how many sit at the extremes. The distribution usually tells a clearer story than any single summary number.
Jamieson raised this in Medical Education when criticizing the routine use of means on Likert scales: these are ordered categories, not equal distances, so the median and the full distribution describe the responses better than the average.
Segment to see where groups differ
Overall numbers can flatten out real differences. Cross-tabulate your results by the groups that matter to you, such as region, role, plan, or how recently someone became a customer, and you often find that a single segment is driving the headline figure.
Segments only work if each one has enough base. When you split 200 responses into six groups, some will be too small to trust, so read those as directional. Live results help here: you can watch a segment fill up and wait until it is large enough before drawing a conclusion.
Cochran established this in Sampling Techniques when addressing domain estimation: the precision of a subgroup figure depends on the number of responses falling within that subgroup, not on the total sample size.
Find the why, and stay honest about cause
Closed questions tell you what is happening; open-text answers tell you why. Read a sample of the comments before you settle on any interpretation, because a single recurring complaint can explain a drop that the charts only hint at.
Two cautions keep your reading honest. First, a link between two things is association, not proof that one caused the other, because a hidden factor may drive both. Second, watch for non-response bias: the people who ignored your survey may differ from those who answered, and the happiest or busiest voices are often the ones missing.
And chasing a high response rate isn't enough on its own: the Groves and Peytcheva meta-analysis in Public Opinion Quarterly, which pooled 59 methodological studies designed to measure nonresponse bias, concluded that response rate does not generally predict that bias.