IB Chemistry Internal Assessment Guide

The IB Chemistry Internal Assessment (IA) is your opportunity to think and work like a real scientist, contributing 20% of your final HL or SL grade. It moves you beyond prescribed labs, challenging you to design, execute, and analyze an original investigation that demonstrates your mastery of chemistry. A successful IA showcases not just your content knowledge, but your skills in critical thinking, procedural planning, and analytical communication, forming a core pillar of your IB science experience.

Designing a Focused and Original Research Question

A well-crafted research question is the non-negotiable foundation of a high-scoring IA. It defines your investigation's scope, direction, and scientific merit. The key is to be specific, manageable, and chemistry-focused. For example, instead of the vague "How does temperature affect reactions?", a strong question would be: "How does temperature (15.0°C to 35.0°C) affect the initial rate of the iodination of propanone, as determined by the change in absorbance per second?" This version explicitly states the manipulated variable (temperature), the measured outcome (initial rate via absorbance change), and a clear context (a specific chemical reaction).

Your question must clearly identify the variables. The independent variable is what you deliberately change (e.g., concentration, temperature, surface area). The dependent variable is what you measure in response (e.g., rate of reaction, pH, temperature change). You must also identify all relevant controlled variables—the conditions you must keep constant to ensure a fair test. Originality can stem from a novel combination of variables, applying a known technique to a new context, or investigating a local environmental or product chemistry issue. The goal is to create a question that is personal, yet grounded in established chemical principles, allowing for meaningful data collection and analysis.

Planning a Method for Reliable Data Collection

With a strong question in hand, you must design a method that generates reliable and sufficient data. Your plan is a detailed, numbered list of instructions, written in the future tense, that someone else could follow exactly. It must justify every key choice: Why use a colorimeter instead of titration? Why a specific acid concentration? Why five repeats? For every measurement, you must specify the precise equipment (e.g., "a 50.00 ± 0.05 mL volumetric flask" instead of just "a flask"), as this directly links to your later error analysis.

The inclusion of appropriate controls is critical. A control experiment validates that your measured effect is due to your independent variable. For instance, if investigating the effect of a catalyst, you must run a trial without the catalyst under identical conditions. Your plan must also explicitly address safety and ethical considerations, listing specific hazards (e.g., "1.0 M hydrochloric acid is corrosive") and corresponding precautions ("wear gloves and safety goggles"). Finally, state how you will collect enough data for a valid conclusion—typically a minimum of five different values for your independent variable, each with at least three repeats to identify anomalies and calculate a meaningful average.

Processing Data and Graphical Analysis

Data processing transforms your raw measurements into the quantities needed to answer your research question. This often involves calculations using chemical formulas. For example, you may convert absorbance to concentration using a calibration curve, calculate reaction rates from time data, or determine enthalpy changes using $q=mc\Delta T$. You must show one complete, annotated sample calculation for each type of processing. Present all processed data in clear, well-formatted tables with appropriate units and significant figures.

Graphical analysis is where trends and relationships become clear. You must choose the correct graph type: typically a scatter plot for continuous data. The independent variable goes on the x-axis, the dependent on the y-axis. Plot the mean of your repeats and include error bars representing the range or standard deviation of your data. Your analysis must interpret the graph's shape. Is the relationship linear? If so, draw a line of best fit (not connecting the dots) and state its equation. Use the line's gradient or intercept to extract quantitative information relevant to your question, such as a rate constant or enthalpy value. The graph is a primary tool for discussion, so your commentary must be thorough.

Evaluating Errors and Structuring the Report

An honest and insightful error evaluation distinguishes a good IA from a great one. Do not just list generic errors. Quantify uncertainty where possible: if using a measuring cylinder with a ±0.5 mL tolerance, calculate the percentage uncertainty ($\frac{\text{absolute uncertainty}}{\text{measurement}}\times 100$). Identify systematic errors (e.g., a consistently uncalibrated pH meter shifting all readings) and random errors (e.g., slight variations in mixing time). Most importantly, evaluate the impact of each significant error on your results and link it directly to your conclusion's validity. Suggest specific, realistic improvements for a future investigation.

Your final report must be structured to meet the IB assessment criteria (Personal Engagement, Exploration, Analysis, Evaluation, Communication). Write in clear, concise scientific language. The introduction provides background theory and justifies your research question. The conclusion directly answers that question, supported by your processed data. Finally, the discussion interprets your results in the context of chemical theory, explains discrepancies from expected values using your error analysis, and explores potential extensions of your work. Your entire investigation should tell a coherent scientific story from question to conclusion.

Common Pitfalls

1. The Overly Ambitious or Vague Question: Attempting an investigation with impractically complex equipment or an unclear focus leads to poor data and analysis. Correction: Start simple. A tightly focused question on a classic chemical principle, executed with precision, scores much higher than a sprawling, poorly controlled experiment.
2. Neglecting Controls and Justification: Presenting a method as a simple list of steps without explaining why you chose them shows a lack of deeper scientific understanding. Correction: For every major procedural choice (equipment, concentrations, volumes), include a one-sentence justification linking it to reliability, accuracy, or safety.
3. Graphing and Analysis Errors: Plotting incorrect variables, forgetting error bars, or simply stating "the graph goes up" without quantitative interpretation will severely limit your score. Correction: Always plot the processed variable that answers your question. Use error bars. Analyze the graph's numerical features (gradient, intercept, shape) and state explicitly what they tell you about the chemistry.
4. Superficial Error Evaluation: Listing "human error" or "measurement error" is inadequate. It shows no critical reflection. Correction: Identify specific, chemistry-relevant errors. Quantify instrument uncertainties. Critically discuss whether each error would increase or decrease your final result and how it affects the confidence in your conclusion.

Summary

• Your IA is an original investigation built on a specific, chemistry-focused research question that clearly defines independent, dependent, and controlled variables.
• A successful method is a justified, detailed procedure that prioritizes reliability, includes controls, and specifies equipment to enable thorough error analysis later.
• Data processing must be shown with sample calculations, leading to graphical analysis that uses trend lines and error bars to extract quantitative meaning.
• A high-scoring evaluation quantifies uncertainties, distinguishes between systematic and random errors, and links their impact directly to the validity of the conclusion.
• The final report must be a well-structured, coherent narrative that addresses all IB assessment criteria, from personal engagement through to clear communication.