Plot Experiment Results

TLDR

An experiment results line chart plots measured values against an independent variable (time, concentration, temperature, etc.), making it straightforward to visualize relationships, compare experimental conditions, and identify trends or anomalies. This template includes multi-condition sample data from a reaction kinetics experiment, ready to load and customize.

Overview

The National Science Foundation (NSF) reports that data visualization is cited as a critical skill in over 80% of STEM graduate program requirements. In laboratory settings, plotting raw data as a line chart is often the first step in analysis — it reveals whether the relationship is linear, exponential, or irregular before any curve-fitting is applied.

This template demonstrates a common experimental setup: measuring a dependent variable (reaction yield %) at multiple time points under two different conditions (Catalyst A vs. Catalyst B). The multi-series format lets you visually compare conditions and determine which produces better results, faster.

When to Use This Template

• Lab reports: Create publication-quality figures showing experimental observations alongside controls
• Research presentations: Overlay multiple experimental conditions to demonstrate comparative results
• Data validation: Quickly plot raw data to check for outliers or equipment errors before deeper statistical analysis
• Teaching demonstrations: Show students how to translate raw measurements into meaningful visualizations

Step-by-Step Guide

Step 1: Prepare Your Data

Structure your experimental data in CSV format with columns: time_min (independent variable), yield_pct (measured value), and condition (experimental group). Ensure measurements use consistent SI units. Include all data points, even outliers — you can annotate them rather than removing them.

Step 2: Configure the Chart

Select Line chart type with Long data format. Enable Show Points — in scientific charts, individual data points must be visible (this is a standard requirement in most journals). Consider enabling Smooth only if the underlying phenomenon is continuous; otherwise, keep lines straight to honestly represent measurement intervals.

Step 3: Customize and Export

For journal submissions, export as PNG at 2x resolution with a white background. Use solid lines for primary data and dashed lines for controls or theoretical predictions. Label axes with variable name and units (e.g., "Time (min)", "Yield (%)"). If submitting to a journal, verify their figure formatting guidelines.

Sample Data (CSV)

time_min,yield_pct,condition
0,0.0,Catalyst A
5,12.3,Catalyst A
10,28.7,Catalyst A
15,41.2,Catalyst A
20,52.8,Catalyst A
30,68.5,Catalyst A
45,79.1,Catalyst A
60,85.4,Catalyst A
0,0.0,Catalyst B
5,8.1,Catalyst B
10,19.4,Catalyst B
15,31.6,Catalyst B
20,42.3,Catalyst B
30,58.9,Catalyst B
45,71.2,Catalyst B
60,78.6,Catalyst B

Best Practices

• Always show data points: Unlike business charts, scientific figures require visible data markers. This lets readers assess data density and identify potential outliers.
• Include error bars when available: If you have replicate measurements, calculate standard deviation and mention it in annotations or a table alongside the chart.
• Use descriptive axis labels with units: "Time" alone is insufficient. Use "Time (min)" or "Reaction Time / min" following your target journal's conventions.
• Maintain aspect ratio: A 4:3 or 16:9 aspect ratio prevents visual distortion of trends. Avoid overly tall or wide charts.

Common Mistakes to Avoid

• Connecting non-continuous data with lines: If your measurements are at discrete, irregular intervals, the connecting line implies interpolation. Add a note or use markers-only mode if this is a concern.
• Removing outliers without justification: Deleting data points that "look wrong" is a form of data fabrication. Instead, include them and annotate with a possible explanation (equipment error, contamination, etc.).

FAQ

What chart type is best for showing experiment results?

A line chart is the standard for experiment results where you plot a measured variable against a controlled independent variable. It clearly shows the relationship, trend, and rate of change. For experiments with discrete categories rather than continuous variables, a bar chart may be more appropriate.

How do I add error bars to my experiment chart?

Prepare your data with additional columns for upper and lower bounds (mean ± standard deviation). In Line Graph Maker, you can use the confidence band chart type to visualize this range, or annotate error ranges in the chart subtitle as is common in preliminary analysis.

Should I use smooth or straight lines for scientific data?

Use straight (linear interpolation) lines unless you have theoretical justification for smoothing. Smooth curves imply a continuous model fit, which may misrepresent discrete measurements. If you do smooth, state the smoothing method used.

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