Explore Data and Explain What You Find

Describe the pattern, inspect the exceptions, and keep the explanation open to evidence.

A service manager asks how long requests take. AI calculates an average of thirteen minutes and drafts a staffing recommendation. The underlying records contain two different kinds of work, plus one unusually long case.

The average is correct. The recommendation may still be poorly grounded.

Exploratory data analysis helps you understand what is in a dataset before settling on an explanation or model. It uses summaries, comparisons, plots, and source checks to reveal structure, unusual values, and questions worth investigating. NIST describes EDA as an approach to gaining insight into data, rather than merely a fixed collection of techniques. See NIST’s introduction to exploratory data analysis.

The most useful AI assistant is one that helps you examine the evidence and makes its calculations reproducible.

Confirm the unit and coverage first

Our fictional dataset contains eight completed service requests from one defined teaching shift. Duration means recorded active handling minutes, excluding waiting time. Each row is one request; none is a repeated update to the same request.

The task categories were assigned by the source process. They are not categories invented after seeing the durations.

All eight durations are present. The packet says nothing about requests still open at the end of the shift, total waiting time, customer satisfaction, or other shifts. Those gaps limit what the analysis can claim.

A completion-only extract can underrepresent slow or unfinished work. Even when our teaching calculation is correct, a real staffing decision would need a suitable account of all relevant workload.

Describe the distribution with more than one number

The durations sum to 104 minutes. Dividing by eight gives a mean of 13 minutes.

The sorted middle values are 4 and 12, so the median is 8 minutes. The minimum is 2, the maximum is 50, and the range is 48 minutes.

The mean is useful for arithmetic workload accounting under the defined measure. The median describes the center of the observed distribution differently. Neither is universally the “correct average” for every question.

If you report quantiles, identify the calculation convention when exact reproducibility matters, especially for a small dataset. Software packages can use different interpolation rules. Do not argue over a numerical difference before checking whether the methods match.

Inspect meaningful groups

The four quick requests total 12 minutes and average 3 minutes. The four detailed requests total 92 minutes and average 23 minutes.

The pooled average of 13 minutes sits between two quite different groups. It does not describe a typical quick request particularly well, and it conceals the spread among detailed requests.

The detailed group has durations 12, 14, 16, and 50. Its median is 15 minutes. That makes the long case visible without deleting it.

Segment using categories that have a meaningful basis. If you try dozens of arbitrary groupings until a dramatic pattern appears, label the exercise as exploratory and evaluate the pattern with appropriate additional evidence. A striking subgroup discovered after extensive searching is not automatically a confirmed finding.

Also report group sizes. A subgroup average based on one case should not appear to carry the same evidential weight as a well-observed group.

Investigate the unusual value

S08 is much longer than the other requests. The first question is whether the value is correctly recorded.

For this teaching packet, suppose the source reviewer confirms that the request involved several documented actions and that fifty active minutes is valid under the stated definition. Keep it in the main analysis.

You can show a sensitivity calculation: excluding S08 would leave 54 minutes across seven requests, for a mean of approximately 7.71 minutes. Label that as a comparison demonstrating influence, not a corrected result.

The difference tells you that S08 strongly affects the mean. It does not justify discarding the case because it makes the performance report less flattering.

If the source review instead established a recording error, document the correction and rerun the analysis with a versioned change log. Article 094’s distinction between unusual and invalid values applies here.

Compare time periods without losing the mix

A second, separate fictional example illustrates how group composition can change a headline. It is not an extension of the eight requests above.

The overall calculations are:

Period A: (8 × 4 + 2 × 20) ÷ 10 = 7.2 minutes.

Period B: (2 × 3 + 8 × 18) ÷ 10 = 15 minutes.

Both category means are lower in Period B, yet the overall mean is higher because detailed work makes up a much larger share of the requests.

The aggregate and group results answer different questions. “Average recorded duration per request rose” is true. “Every type of work became slower” is false in this packet.

A standardized comparison could hold category weights fixed to examine one aspect of the change. It would answer a deliberately different question from the actual observed average, so document the weights and purpose rather than replacing the observed result without explanation.

Separate findings from hypotheses

A finding states what the records show: “The detailed category has a higher observed mean, and one valid long request raises its average.”

A hypothesis proposes an explanation: “Detailed requests may require more supporting documents.” That needs relevant records or a new measurement.

An action proposal goes another step: “Create a specialized intake checklist.” That may be worth testing, but the duration table alone does not establish that the checklist will help.

Ask AI to produce these as separate sections. This prevents a plausible narrative from traveling into a report as an observed fact.

When the data surprises you, examine definitions and coverage before inventing a behavioral explanation. A change in recording practice can look like a change in performance.

Build an analytical narrative a reader can check

A useful short report begins with the most decision-relevant observation, then provides the supporting numbers and limitations.

For the eight-row packet:

“Completed requests used 104 recorded active minutes across eight cases. The overall mean was 13 minutes and the median was 8. Quick requests averaged 3 minutes, while detailed requests averaged 23. One verified 50-minute detailed request had substantial influence on the overall mean. These records describe completed active work in one teaching shift; they do not establish waiting time, unfinished workload, or the causes of the category differences.”

The report does not need dramatic language. Its usefulness comes from giving the reader a defensible picture and a clear next question.

An appropriate next step might be to examine detailed-request subtypes or include open requests and waiting time. Choose the follow-up based on the actual decision, not on whichever new chart AI can generate most easily.

Keep the exploration reproducible

Save the input version, definitions, formulas or code, filters, and output table. If you create a chart, preserve the data used to draw it and the relevant settings.

A reviewer should be able to reproduce 104, 13, and 8 from the eight rows. They should also be able to tell that the two-period mix example is a different dataset.

Avoid overwriting the source while sorting or grouping. Preserve a stable identifier so a plotted outlier can be traced back to the original record.

Document exploratory decisions. If you looked at category, hour, and request type, record those views rather than showing only the one that supported your preferred explanation.

A reusable prompt

Explore this dataset using its defined unit, coverage, and categories. Calculate counts, totals, central values, spread, and missingness with reproducible formulas. Inspect meaningful groups and time periods, and trace unusual values to source records before excluding them. Separate observed findings, hypotheses, and proposed actions. Identify composition changes that could mislead an aggregate comparison. Write a short narrative with supporting numbers, limitations, and the next evidence question.

For students: explain two true summaries

Calculate the mean and median before asking AI. Then explain why thirteen minutes and eight minutes can both describe the same dataset without either being an error.

Statistics students can examine influence and weighting. Business students can discuss why active handling time is different from customer waiting time. Science students can connect the same reasoning to repeated measurements and heterogeneous samples.

Your submission should include one source check and one rejected explanation. Showing why the evidence does not establish a claim is part of learning analysis.

Practice: write the shift report

Use S01–S08 to produce a one-page exploratory report. Include total, mean, median, category summaries, and the verified status of S08. Add a clearly labeled sensitivity calculation excluding S08, while preserving the main result with all valid records.

Then explain the second table’s increase from 7.2 to 15 minutes without claiming that either category became slower.

Completion check: The eight-row total is 104, mean 13, median 8, and category means 3 and 23. The long valid case remains in the primary analysis. The period comparison accounts for changed group proportions, and the narrative separates observations from explanations.

Stretch: Recalculate both periods using equal category weights. The standardized means are 12 and 10.5 minutes. Explain why that comparison is useful and why it should not be presented as the actual observed average workload per request.