Data Presentation and Evaluation

Present observations and data using appropriate and clear formats.
When processing data, use IUPAC chemical nomenclature and SI units (for example kg, g, mg; km, m, mm; kJ, J).
Apply order of magnitude prefixes (e.g., tera, giga, mega, kilo, centi, milli, micro, nano) and powers of ten.
Translate data effectively between various representational forms.
Interconvert units accurately during numerical data processing.
Apply an appropriate number of significant figures in all calculations.
Construct graphs with suitable axes, scales, plotted points, lines of best fit, and uncertainty indicators (e.g., range bars).
Analyze quantitative data to identify clear patterns and trends.
Calculate descriptive statistics such as the range and mean from data sets.
Extract values from graphs, including calculating gradients and performing interpolation or extrapolation.
Evaluate data accuracy, precision, repeatability, and reproducibility in a given scientific context.
Identify potential sources of random and systematic errors in experimental data.
Discuss and justify decisions to discard or retain outlier data points.
Evaluate experimental strategies and suggest improvements to increase overall data quality.
Interpret data across diagrammatic, graphical, symbolic, or numerical forms to draw reasoned scientific inferences.
Assess the extent to which collected data increases or decreases confidence in a scientific prediction or hypothesis.

What you need to know