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.

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Data and spreadsheets

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

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Graph design and uncertainty

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

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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.

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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.Cubic spline fittingCubic spline curve fitting: knots, smoothness, and when to use itLearn how a cubic B-spline fits curved scientific data with connected local polynomials, how region boundaries affect the result, and when a spline is safer than one high-degree polynomial.Polynomial regressionHow to choose a polynomial regression degreeCompare linear, quadratic, cubic, and higher-degree polynomial fits using scientific reasoning, residual patterns, and validation evidence instead of R-squared alone.Fourier fittingFourier curve fitting for periodic and repeating dataUse Fourier series to model repeating scientific signals, choose the number of terms carefully, and distinguish a genuine period from a flexible fit to noise.Exponential and power fitsExponential fit vs power-law fit: how to chooseDistinguish exponential growth or decay from a power-law relationship using the scientific mechanism, transformed plots, residuals, and valid data domains.

Statistical comparisons

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

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Export and saving

Preserve reproducible outputs and return to saved projects later.

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