1. Clear and Descriptive Title: A concise title that indicates the relationship being investigated. Avoid vague titles like "My Experiment." Instead, use titles like "Effect of Temperature on the Initial Rate of Reaction Between A and B."
2. Clearly Labeled Axes with Units: Both the x-axis and y-axis must be clearly labeled with the name of the variable and its appropriate units in parentheses. For example, "Temperature (°C)" or "Absorbance (arbitrary units)".
3. Appropriate and Linear Scale:
4. Choose scales for both axes such that the plotted data points occupy most of the graph paper, making the trend clear and allowing for easy reading of values. Avoid compressing data into a small corner or stretching it excessively.
5. The scale must be linear; i.e., equal divisions on the axis must represent equal increments in the variable's value. Do not break the scale unless absolutely necessary and clearly indicated.
6. Accurately Plotted Data Points: Plot each data point precisely at its correct coordinates. Use small crosses or distinct dots.
7. Best-Fit Line or Curve:
8. This is a smooth line or curve that represents the overall trend of your data. It is a visual average of the plotted points.
9. Crucially, do NOT "connect the dots" unless there is a theoretical reason to believe that every point lies precisely on the line and that there are no errors (e.g., in a simple Beer-Lambert plot where theoretical linearity is expected).
10. The best-fit line should be drawn such that it balances the points above and below the line, minimizing the overall distance of the points from the line.
11. Extrapolation: Extending the best-fit line/curve beyond the range of your measured data points. This is used to predict values outside the experimental range. Extrapolation should be done with caution, as the established trend may not hold true beyond the measured limits.
12. Interpolation: Reading values from the best-fit line/curve within the range of your measured data points. This is generally more reliable than extrapolation because you are within the observed data range.

Graphs allow for qualitative and quantitative analysis of experimental data:

• Identifying Trends: Visually observe if there is a positive correlation (as x increases, y increases), a negative correlation (as x increases, y decreases), or no clear correlation.
• Determining Linear Relationships: If your graph is a straight line, it indicates a direct linear relationship between the variables. The equation of a straight line is y=mx+c, where:
• Gradient (Slope, m): Represents the rate of change of the dependent variable (y) with respect to the independent variable (x). Calculate it using two widely separated points on the best-fit line, not necessarily actual data points. Include appropriate units for the gradient (units of y / units of x).
• Y-intercept (c): The value of y when x is zero. Its chemical significance depends on the experiment. For example, in a Beer-Lambert plot, a zero y-intercept indicates no absorbance at zero concentration.
• Non-linear Relationships: Curved graphs indicate non-linear relationships. Sometimes, these can be "linearized" by plotting different functions of the variables (e.g., plotting 1/rate vs. 1/[concentration] for second-order kinetics, or ln(rate) vs. 1/Temperature for Arrhenius equation).
• Identifying Anomalous Data (Outliers): Data points that fall significantly off the general trend of the best-fit line may be anomalous and warrant re-examination or exclusion (with justification).

Error bars are visual representations on a graph of the uncertainty (random error) associated with each data point.

• Each error bar is a line segment drawn through a data point, extending a distance equal to the absolute uncertainty above and below the point (for uncertainty in the y-variable) or to the left and right (for uncertainty in the x-variable).
• Importance:
• They provide a visual indication of the precision of each individual measurement.
• A best-fit line should be drawn such that it passes within or at least through the majority of the error bars. If the line consistently falls outside the error bars, it suggests that either your uncertainty estimates are too small, or there may be a systematic error.
• The spread of the error bars can be used to estimate the maximum and minimum possible gradients of a linear relationship, thereby providing an uncertainty for the calculated gradient itself. This is a higher-level analysis, particularly valuable for the IA.