Introduction: Why Evaluation Matters

In Chemistry, performing an experiment is only half the battle. The other half—and the part that distinguishes a great chemist—is evaluation. Evaluation is the process of looking at your method and your data and asking: "How much can I actually trust these results?"

In your Unit 5 exam, you will be expected to critique procedures and results from the 10 Required Practicals you've studied. This chapter will help you identify where errors come from, how to calculate exactly how "uncertain" your data is, and how to suggest improvements to make an experiment better. Don't worry if this seems a bit abstract at first; once you learn the patterns, you'll see the same types of errors popping up in almost every experiment!

1. Evaluating the Procedure: Looking for Flaws

When you evaluate a procedure, you are looking at the limitations of the equipment and the method used. A "perfect" experiment is impossible, but we want to get as close as we can.

Systematic vs. Random Errors

Systematic Errors: These are errors that happen every single time you repeat the experiment. They shift your results in the same direction (e.g., all your readings are too high).
Example: A thermometer that is poorly calibrated and always reads \(1.0^{\circ}\text{C}\) higher than the actual temperature.

Random Errors: These are unpredictable. They vary every time you take a measurement and can make your results higher or lower than the true value.
Example: Judging the exact moment a solution changes color in a titration (the end-point).

Common Procedural Limitations

  • Heat Loss: In calorimetry (Required Practical 2), heat lost to the surroundings is the biggest source of error. This makes the experimental \(\Delta H\) value lower than the theoretical value.
  • Incomplete Reactions: In organic synthesis (Required Practical 10), you might not get a 100% yield because the reaction is reversible or side-reactions occur.
  • Measurement Precision: Using a measuring cylinder (less precise) instead of a pipette or burette (more precise).
Quick Review: How to Improve?

If you identify an error, always suggest a specific fix. For heat loss, suggest "adding a lid" or "using a copper calorimeter." For titration, suggest "adding the titrant dropwise near the end-point."

2. Evaluating the Results: Uncertainties

  • Addition or Subtraction: Add the absolute uncertainties (e.g., \(0.1\text{cm}^3 + 0.1\text{cm}^3\)).
  • Multiplication or Division: Add the percentage uncertainties.
  • Powers: If a value is squared, multiply the percentage uncertainty by 2. If it is cubed, multiply by 3.

3. Comparing Results to Theoretical Models

Sometimes, we evaluate results by comparing them to a "perfect" scientific model. A classic example in the A2 syllabus (Section 3.1.8) is Lattice Enthalpy.

We can calculate Lattice Enthalpy in two ways:

  1. Experimental: Using a Born-Haber cycle based on real measurements.
  2. Theoretical: Using the "perfect ionic model" (assuming the ions are perfect spheres with no covalent character).

The Evaluation: If the experimental value is much larger than the theoretical value, it proves the bond has covalent character. The "perfect ionic model" is limited because it doesn't account for the polarization of ions.

4. Identifying Impurities

Evaluating a product's purity is a key skill in organic chemistry (Required Practical 10). You can use your knowledge of Organic Analysis (Sections 3.3.6 and 3.3.15) to evaluate your results.

  • Infrared (IR) Spectroscopy: If your product is supposed to be an alcohol but the IR spectrum shows a sharp peak at \(1700\text{cm}^{-1}\), you have a \(C=O\) impurity.
  • NMR Spectroscopy: Extra peaks in a \(^{1}\text{H}\) or \(^{13}\text{C}\) NMR spectrum that don't match your target molecule indicate the presence of impurities.
  • Melting Point: A pure solid has a sharp melting point at the value stated in data books. If the substance is impure, it will melt over a wide range and at a lower temperature than expected.

5. Yield and Atom Economy

When evaluating a synthetic procedure, we look at efficiency using two calculations from Section 3.1.2:

Percentage Yield: \(\frac{\text{Actual Yield}}{\text{Theoretical Yield}} \times 100\)
Evaluation: Low yield suggests loss of product during transfer, incomplete reaction, or side reactions.

Atom Economy: \(\frac{\text{Mass of Desired Product}}{\text{Total Mass of All Reactants}} \times 100\)
Evaluation: Low atom economy means the process is wasteful, even if the yield is 100%, because many of the reactant atoms end up in "waste" by-products.

Did you know? In modern "Green Chemistry," industrial chemists prioritize high atom economy to reduce waste and save money on disposing of toxic by-products!

Summary Checklist for the Exam

When asked to evaluate an experiment, go through this mental checklist:

  • The "Why": Why is my experimental value different from the book value? (Heat loss? Incomplete reaction? Impurities?)
  • The "How Much": What is the percentage uncertainty? Which piece of equipment contributed the most to the error?
  • The Improvement: How can I change the apparatus or method to reduce these errors?
  • The Anomalies: Are there any data points that don't fit the trend on the graph? (These should be ignored when drawing a line of best fit).

Key Takeaway: Evaluation isn't about saying an experiment was "bad." It's about showing you understand the limitations of your tools and the complexity of chemical reactions. Practice calculating uncertainties and you will find these questions much easier!


Note: For more details on specific techniques, refer to the chapters on Required Practicals 1-10 and Data Handling, Graphs and Uncertainties.