FigureCheck learning centre

Scientific plotting tutorials and practical guides

Learn how to prepare data, choose an honest graph, fit curves, compare groups, review outliers, and export reproducible scientific figures.

Start here

Build a first graph and understand the main FigureCheck workflow.

Getting startedCreate your first scientific figureFigureCheck turns a table of measurements into an editable Matplotlib figure, with fitting, statistical checks, and reproducible exports available when you need them.CSV plottingCreate a scatter plot from CSV data onlineTurn a CSV, TSV, or Excel table into a labeled scientific scatter plot, then check the data mapping before exporting the result.

Data and spreadsheets

Import, enter, calculate, group, and prepare measurements for plotting.

Data and seriesPrepare data and build seriesUse the spreadsheet to correct values, combine related measurements, and decide which observations belong to each visible series.Spreadsheet editingSelect, copy, and fill spreadsheet dataWork with FigureCheck data as a spreadsheet: select ranges, copy and paste blocks, drag values or formulas into nearby cells, and let changes save automatically.Spreadsheet formulasCalculate values with spreadsheet formulasCreate calculated values directly in the FigureCheck table with familiar cell references, arithmetic, ranges, and summary functions.

Graph design and uncertainty

Choose an honest presentation, show uncertainty, and prepare final figures.

Better graphsMake a clearer scientific graphA strong graph makes the comparison obvious, represents uncertainty honestly, and remains readable at the final size.Choosing a graph typeScatter plot or line graph: which should you use?Choose between points and connected lines by asking whether the observations are independent measurements or an ordered continuous sequence.Axis labelsHow to label scientific graph axes and unitsWrite axis labels that identify the measured quantity, its unit, and any transformation without forcing the reader to guess.Scatter plots and uncertaintyCreate a scientific scatter plot with error barsShow the relationship between two measured variables while making the uncertainty and meaning of every point clear.Uncertainty and spreadStandard deviation vs standard error on a graphDistinguish variation among observations from uncertainty in an estimated mean before choosing error bars or shaded bands.Uncertainty calculationsCalculate measurement and gradient uncertaintyChoose an uncertainty that matches the measurement process, propagate it through calculations, and estimate gradient bounds from error bars without overstating precision.IB Physics uncertaintyIB Physics guide to uncertainty and max-min gradientsPresent uncertainty, error bars, maximum and minimum gradients, and a concise worked result suitable for an IB Physics scientific investigation.Logarithmic axesUse logarithmic axes without misleading readersUse a log scale when ratios and orders of magnitude matter, and make the transformed visual interpretation explicit.Publication-ready figuresPrepare a publication-ready scientific figureDesign for the final physical size, verify legibility and accessibility, and export a format appropriate for the journal, report, poster, or presentation.

Fits and data quality

Choose a model, diagnose overfitting, and review unusual observations.

Fits and statisticsInterpret fits and statistical checksFit each visible series independently, inspect validation evidence, and compare groups without treating a single score as a scientific conclusion.Choosing a fitChoose a curve fit and detect overfittingMatch the model to the scientific question, then use residuals and validation evidence to distinguish useful structure from an unnecessarily flexible curve.Fit interpretationHow to interpret R-squared and Pearson correlationUse R-squared and Pearson correlation as limited summaries, not as proof that a model is correct or that one variable causes another.Residual plotsHow to read a residual plot for curve fittingUse residual patterns to identify bias, missing curvature, changing variance, and individual observations that a fit score can hide.Outliers and data cleaningReview and remove outliers responsiblyIdentify unusual observations relative to a stated baseline, inspect them individually, and preserve a defensible record of any exclusion.

Statistical comparisons

Compare visible groups with tests and effect sizes that match the design.

Comparing two groupsCompare two independent groups statisticallyCompare two visible series with a test that matches the study design, then report the effect size and uncertainty alongside the p-value.Choosing a statistical testWelch t-test vs Mann-Whitney U testChoose an independent-group comparison by matching the scientific question and sampling design, then report effect size alongside the p-value.

Export and saving

Preserve reproducible outputs and return to saved projects later.

ExportingExport a reproducible resultSet the final dimensions and resolution for the destination, then export the figure together with the information needed to reproduce or audit it.Saving your workSave and reopen your FigureCheck workFigureCheck keeps a recent browser copy automatically and lets you create named projects, with optional private cloud sync after you sign in.

FigureCheck Web v1.0.4 (20260805-002332)Start here