est-data-analysis

Data Analysis (est-data-analysis)

ES&T reviewers scrutinize the analytical chain: blanks, recoveries, detection limits, replicates, and whether the numbers add up. This skill covers execution and reporting; design decisions live in est-study-design, and deposit/reproducibility in est-reporting-and-reproducibility.

When to trigger

• Reducing raw instrument/field data into results
• Building the results section and the QA/QC reporting
• A reviewer asked about detection limits, recoveries, replicates, or statistics
• Closing a mass/energy balance or fitting kinetics/dose–response

Analysis norms ES&T expects

1. Report QA/QC explicitly. Method/field blanks, matrix-spike recoveries, CRM results, LOD/LOQ, calibration range and R², surrogate/internal-standard recoveries, and how non-detects were handled.
2. Honest uncertainty. Report replicates with measures of dispersion (SD/SE/CI), not single values; propagate uncertainty through derived quantities; state n every time.
3. Right statistics for environmental data. Handle left-censored (below-LOD) data correctly (e.g., substitution caveats, MLE/ROS, Kaplan–Meier); check distributional assumptions; use nonparametric or transformed analyses for skewed/heteroscedastic data; correct for multiple comparisons.
4. Mass / energy balance. Account for products, sorbed and volatilized fractions, and losses; report closure (%) and explain gaps.
5. Kinetics / dose–response. Report rate constants/half-lives with CIs and goodness of fit; EC/IC/LC values with confidence bounds; state the model fitted.
6. Effect size & significance. Give magnitudes and intervals, not p-values alone; relate results to environmentally meaningful thresholds.

Reproducibility while you work (not at the end)

• A master script/workflow regenerates every figure, table, and SI exhibit from processed data.
• Set and report seeds for any stochastic step (bootstrap, Monte Carlo, simulation).
• Pin software/package versions; record instrument settings and integration parameters.
• Keep figure/table numbers matched to script outputs (see est-reporting-and-reproducibility).

QA/QC reporting table reviewers expect to see

ES&T referees often work down a mental analytical checklist. Reporting each item pre-empts the most common "rigor not established" objection. The minimum set, with what a reviewer reads when it is absent:

Element	What to report	If omitted, the reviewer assumes
Method/field blanks	blank levels vs. sample levels; subtraction approach	contamination is uncontrolled
Recoveries	matrix-spike % and CRM agreement	quantitation is biased/unknown
LOD/LOQ	derivation (e.g., 3σ/10σ or S/N) and per-analyte values	"detections" may be noise
Calibration	range, R², whether samples fall in range	extrapolation beyond standards
Surrogate/IS recoveries	per-sample recovery correction	run-to-run drift hidden
Non-detects	censoring method (ROS/MLE/KM), not bare substitution	summary statistics distorted

Worked micro-example (illustrative — PFAS quantitation with censored data)

A river-PFAS dataset (illustrative numbers) shows how the rules combine into a reportable result:

• 24 samples, triplicate injection; LOQ for PFHxA = 0.5 ng/L (illustrative, derived at 10×S/N).
• Matrix-spike recovery 92% (RSD 7%, n=6); field blank < LOQ; surrogate-corrected.
• 9 of 24 below LOQ — left-censored. Naive half-LOQ substitution would report a mean of 3.1 ng/L; regression-on-order-statistics (ROS) gives 2.4 ng/L (illustrative), because substitution inflated the low tail. Report the ROS mean with its CI and state the method.
• Reported result: "PFHxA = 2.4 ng/L (95% CI 1.7–3.3, n=24, 38% < LOQ; ROS), recovery 92±7%." That single line carries magnitude, uncertainty, n, censoring handling, and recovery — the form a reviewer can sign off without a query.

Referee-pushback patterns and the venue-specific fix

• "Detection limits and QA/QC are not reported." → Add the blank/recovery/LOD/LOQ table to the SI and cite it from Methods; never leave it implicit.
• "Below-detect data handled by substitution." → Re-analyze with ROS/MLE/Kaplan–Meier; show the result is robust to the censoring choice.
• "The mass balance does not close." → Report closure %, name the unaccounted fraction (sorbed, volatilized, mineralized), and bound it rather than ignoring the gap.

Anti-patterns

• Results with no blanks, recoveries, or detection limits reported
• Single measurements with no replication or dispersion
• Naive zero/half-LOD substitution for heavily censored data with no caveat
• A transformation/treatment study whose mass balance never closes (or is never reported)
• p-values without effect sizes or environmental thresholds
• Over-fitting kinetics/dose–response with too few points

Output format

【Main result】magnitude + uncertainty (n, SD/CI) + units
【QA/QC】blanks / recoveries / CRM / LOD-LOQ / calibration reported? [Y/N]
【Censored data】handled how
【Mass/energy balance】closure % + explanation
【Statistics】appropriate test + assumptions checked? [Y/N]
【Reproducible】master script + seeds + pinned versions? [Y/N]
【Next】est-figures-and-tables

Supplementary resources

• ../../resources/external_tools.md — analytical QA/QC, statistics, and modeling packages
• ../../resources/official-source-map.md — data-availability and reproducibility expectations