Exploring & Designing Investigations

SECTION 1

Historical Context & Motivation

Science has not always relied on carefully controlled experiments. For centuries, natural philosophers depended on casual observation and philosophical reasoning to explain the world. The idea that you should deliberately change one thing and measure the effect — what we now call a controlled experiment — developed gradually over hundreds of years. Understanding this history helps us appreciate why the IB Chemistry course places so much emphasis on thoughtful investigation design.

1021

Ibn al-Haytham's Optics

The Arab scholar Ibn al-Haytham (Alhazen) systematically tested hypotheses about light using controlled setups, often called one of the earliest examples of the modern scientific method.

1620

Francis Bacon's Novum Organum

Bacon formalized inductive reasoning and advocated for systematic observation and experimentation, laying the philosophical groundwork for experimental science.

1774

Lavoisier's Quantitative Chemistry

Antoine Lavoisier introduced careful measurement and controlled variables to chemical investigations, disproving the phlogiston theory and establishing the law of conservation of mass.

1935

Fisher's Design of Experiments

Ronald Fisher published foundational work on statistical experimental design, introducing concepts like randomization and replication that remain central to modern scientific investigations.

2013–Present

IB Inquiry-Based Approach

The IB Chemistry curriculum places student-designed investigations at the heart of assessment, requiring learners to explore, design, and evaluate experiments as part of the Internal Assessment (IA).

The central question that investigation design addresses is deceptively simple: How do we set up an experiment so that our results actually answer the question we are asking? A poorly designed investigation can produce data that looks impressive but tells us nothing reliable. This section of the IB Chemistry course equips you with the tools to move from a vague curiosity to a rigorous, testable plan.

SECTION 2

Core Principles of Investigation Design

Designing a good investigation in IB Chemistry is like building a house: you need a solid blueprint before you start construction. The blueprint includes a clear question, well-defined variables, and a plan for collecting reliable data. The following foundational ideas form the framework that every strong investigation is built upon.

Research Question

A focused, testable question that specifies the independent variable (what you change) and the dependent variable (what you measure). Avoid vague or overly broad questions.

Variables

Every investigation involves three types: the independent variable (IV), the dependent variable (DV), and controlled variables (CVs) that must be kept constant.

Hypothesis

A testable prediction that proposes a specific relationship between the IV and DV. A strong hypothesis includes a scientific justification explaining why you expect that relationship.

Control of Variables

Keeping all factors constant except the IV ensures a fair test. This is the single most important design principle. Without it, you cannot attribute changes in the DV to the IV.

Reliability & Reproducibility

Repeating trials (at least 5 per IV value in the IB) reduces the impact of random errors. Reliability means consistent results; reproducibility means another person can replicate your method and get similar results.

✦ KEY TAKEAWAY

Think of designing an investigation like a cooking competition. The independent variable is the one ingredient you are testing (say, the type of oil). The dependent variable is the outcome you judge (the crispiness of the food). The controlled variables are everything else — temperature, cooking time, amount of food — that you keep the same so the comparison is fair. If you changed the oil AND the temperature at the same time, you would never know which one made the difference.

SECTION 3

Visual Explanation — The Investigation Design Flowchart

The six stages of investigation design form a cycle. After evaluating results (stage 6), you may return to the research question (stage 2) to refine your approach. Notice the feedback loop shown by the dashed red arrow — real science is iterative, not linear.

The flowchart above captures the full cycle of investigation design as expected in IB Chemistry. Notice that the process starts with genuine curiosity — observing something interesting in the lab or in the world — and then progressively narrows into a precise, testable structure. Each stage depends on the one before it: a vague research question leads to vague variables, which leads to unreliable data. The feedback loop from evaluation back to the research question reflects the reality that investigations rarely go perfectly the first time. Strong IB students embrace this iterative process and use it to strengthen their experimental design.

SECTION 4

How It Works — From Question to Data

Crafting a Strong Research Question

A good IB Chemistry research question is specific enough to test in a school laboratory and broad enough to generate meaningful data. It explicitly names the independent variable and the dependent variable. Compare these two examples:

Comparison of weak vs. strong IB Chemistry research questions

Weak Question	Strong Question
What affects the rate of a reaction?	How does the concentration of hydrochloric acid (0.5–2.5 mol dm⁻³) affect the rate of reaction with magnesium ribbon, measured by the volume of hydrogen gas produced in 60 seconds?
Is temperature important in dissolving?	How does the temperature of water (20–80 °C in 15 °C increments) affect the time taken for 5.0 g of potassium nitrate to dissolve completely?

Identifying Variables

Once you have a strong research question, list your variables explicitly. The independent variable (IV) is what you deliberately change across trials. The dependent variable (DV) is what you measure in response. Controlled variables (CVs) are all the other factors that could influence the DV; you must hold them constant. For IB purposes, you should aim to have at least five distinct values of the IV and at least five repeated trials at each value.

Quantitative Considerations

MINIMUM DATA POINTS

Total measurements = (number of IV values) × (number of trials per IV value)

For a strong IB IA, aim for at least 5 IV values × 5 trials = 25 raw data points minimum. More data strengthens reliability.

PERCENTAGE UNCERTAINTY

% uncertainty = (absolute uncertainty ÷ measured value) × 100%

When choosing apparatus, select instruments whose percentage uncertainty is small relative to the measurement. For example, a 50 cm³ burette (±0.05 cm³) gives lower % uncertainty than a 10 cm³ measuring cylinder (±0.5 cm³) when measuring 25 cm³.

RANGE OF IV

Range = maximum IV value − minimum IV value

Choose an IV range that is wide enough to reveal a clear trend. If temperature is your IV, testing between 20 °C and 80 °C is much more informative than between 20 °C and 30 °C.

SECTION 5

Detailed Breakdown — Types of Variables and Errors

The top row shows the three types of variables: the independent variable causes a change in the dependent variable, while controlled variables are held fixed. The bottom row distinguishes random errors from systematic errors — two concepts that directly affect how you evaluate your investigation.

Understanding the difference between random errors and systematic errors is essential when evaluating the quality of your data. Random errors cause your measurements to scatter unpredictably around the true value. They reduce precision but can be minimized by repeating trials and averaging results. Systematic errors, by contrast, shift all your data in one direction — they reduce accuracy. No amount of repetition fixes a systematic error; you must identify its source and correct it. In your IB IA evaluation, examiners want to see you distinguish between these two error types.

💡 IB TIP

When listing controlled variables in your IA, do not just name them — explain how you will keep each one constant and why it matters. For instance, instead of writing "temperature was controlled," write "the water bath was maintained at 25 ± 1 °C using a thermostat because temperature affects reaction rates according to collision theory."

SECTION 6

Worked Example — Designing an Investigation from Scratch

Let us walk through a complete investigation design for a classic IB Chemistry experiment: investigating the effect of concentration on reaction rate using the reaction between sodium thiosulfate and hydrochloric acid (the 'disappearing cross' experiment).

Designing the Disappearing Cross Experiment

Step 1 — Formulate the Research QuestionStart with an observation: when sodium thiosulfate (Na₂S₂O₃) reacts with hydrochloric acid (HCl), a yellow sulfur precipitate forms and the solution becomes cloudy. Ask yourself: what factor could I change that would affect how fast the cloudiness appears? Concentration is a natural choice.

Research question: How does the concentration of sodium thiosulfate (0.04–0.20 mol dm⁻³) affect the time taken for a cross viewed through the reaction mixture to become invisible at 25 °C?

Step 2 — State the HypothesisBased on collision theory, increasing the concentration of Na₂S₂O₃ increases the number of reactant particles per unit volume, raising the frequency of successful collisions. Therefore, we predict:

Hypothesis: As the concentration of Na₂S₂O₃ increases, the time for the cross to disappear will decrease because a higher concentration increases the rate of effective collisions.

Step 3 — Identify VariablesIV: Concentration of Na₂S₂O₃ (0.04, 0.08, 0.12, 0.16, 0.20 mol dm⁻³ — five values). DV: Time in seconds for the cross to disappear. CVs: Volume of Na₂S₂O₃ solution (50 cm³), volume and concentration of HCl (5 cm³ of 2.0 mol dm⁻³), temperature (25 ± 1 °C using water bath), same observer judging the endpoint, same marked cross on white paper.

Five IV values, one measurable DV, and at least five explicitly controlled variables.

Step 4 — Design the MethodPrepare each concentration by diluting stock Na₂S₂O₃ with distilled water, keeping the total volume at 50 cm³. Place the conical flask on the cross, add HCl, start the stopwatch, and record the time when the cross is no longer visible. Repeat each concentration five times.

Total measurements: 5 concentrations × 5 trials = 25 data points.

Step 5 — Plan Data ProcessingCalculate the mean time for each concentration. Since rate ∝ 1/time, plot a graph of 1/time (s⁻¹) on the y-axis against concentration (mol dm⁻³) on the x-axis. If collision theory holds, the graph should show a linear or proportional relationship.

Graph: 1/time vs. concentration → expect a straight line through the origin, confirming a directly proportional relationship.

Step 6 — Safety and Ethical ConsiderationsHCl is corrosive — wear safety goggles and gloves. Na₂S₂O₃ is a mild irritant. The reaction produces sulfur dioxide (SO₂), which is toxic in enclosed spaces, so conduct the experiment in a well-ventilated area or fume cupboard. Dispose of waste in designated containers.

A complete risk assessment is included: hazards identified, precautions specified, and disposal methods described.

SECTION 7

Strengths and Limitations of Investigation Designs

No investigation is perfect. Part of demonstrating strong scientific skills in IB Chemistry is being able to honestly evaluate the strengths and weaknesses of your own experimental design. The table below outlines common strengths and limitations you should consider.

Evaluating common features of school-level investigation designs

Design Feature	Strengths	Limitations
Range of IV values	A wide range reveals overall trends and allows detection of non-linear behavior.	Too wide a range may push beyond safe or practical limits; too narrow a range may not reveal a trend.
Number of trials	Repeated trials reduce the effect of random errors and allow calculation of mean values and standard deviation.	Time-consuming; some reactions use expensive or hazardous reagents.
Control of variables	Tight control ensures any change in DV can be attributed to the IV, supporting a valid conclusion.	Perfect control is impossible in practice; some CVs (e.g., ambient humidity) are difficult to regulate.
Choice of apparatus	Using precise instruments (e.g., digital balances ±0.01 g, burettes ±0.05 cm³) reduces measurement uncertainty.	Precise equipment may not always be available in school labs; cost and training constraints.
Subjective endpoints	Simple to perform in a school lab and requires minimal equipment.	Different observers may judge the endpoint differently (e.g., 'when the cross disappears'), introducing random error.

✦ KEY TAKEAWAY

Acknowledging limitations is not a weakness — it is a strength. Think of it like a GPS that tells you its accuracy: "accurate to ±5 meters." A GPS that claims perfect accuracy is less trustworthy than one that honestly reports its margin of error. In the same way, an IB investigation that candidly identifies sources of error and proposes improvements earns higher marks than one that pretends everything went perfectly.

SECTION 8

Connection to the IB Internal Assessment and Advanced Research

The skills you develop in this topic are directly assessed in the IB Chemistry Internal Assessment (IA), which accounts for 20% of your final grade. The IA requires you to design, execute, and evaluate a complete investigation — essentially applying every principle from this lesson in a single piece of work. Beyond the IB, these same skills underpin all professional scientific research.

How investigation design scales from school to professional science

Aspect	School-Level Investigation	Professional Research
Research question	Single IV and DV; clear, focused scope.	May involve multiple IVs and DVs; literature review informs question.
Variables	5+ IV values, ≥5 trials; manual control of CVs.	Automated instruments; factorial designs testing many variables simultaneously.
Error analysis	Percentage uncertainty, qualitative error discussion.	Statistical tests (t-tests, ANOVA, regression); propagation of uncertainty formulas.
Reproducibility	Method described so a classmate could replicate it.	Peer-reviewed publication; raw data often shared openly.
Ethics & safety	Risk assessment; teacher approval before lab work.	Institutional review boards; environmental impact assessments; regulatory compliance.

As you progress to university-level chemistry, you will encounter more sophisticated experimental designs, such as factorial experiments that test multiple independent variables simultaneously, and blind studies where the experimenter does not know which sample is which until after data collection. These advanced techniques all build on the same core logic you are learning now: identify what you are testing, control everything else, and collect enough data to draw a valid conclusion.

SECTION 9

Practice Problems

PROBLEM 1 — CONCEPTUAL

A student wants to investigate how temperature affects the rate of reaction between magnesium and hydrochloric acid. She measures the volume of hydrogen gas produced. Identify the independent variable, the dependent variable, and name three controlled variables she should keep constant.

PROBLEM 2 — BASIC CALCULATION

A student uses a 50 cm³ burette (uncertainty ±0.05 cm³) to measure 25.00 cm³ of solution. Calculate the percentage uncertainty in this single measurement.

PROBLEM 3 — INTERMEDIATE

A student collects the following times (in seconds) for a cross to disappear at a Na₂S₂O₃ concentration of 0.12 mol dm⁻³: 34, 31, 38, 33, 35. Calculate the mean time and explain whether these results suggest good precision, good accuracy, or both. What additional information would you need to comment on accuracy?

PROBLEM 4 — APPLIED

You are designing an IB Chemistry IA to investigate how the surface area of calcium carbonate (CaCO₃) affects the rate of its reaction with hydrochloric acid. Write a complete research question, state a hypothesis with a scientific justification, and outline how you would vary the IV while controlling at least four other variables.

PROBLEM 5 — CRITICAL THINKING

Two students design the same investigation but obtain very different results. Student A uses three trials per IV value with four IV values. Student B uses six trials per IV value with seven IV values. Both students identify the same systematic error (an uncalibrated thermometer reading 3 °C too low). Explain which student's data set is more reliable and which is more accurate. Then propose a way to correct the systematic error and explain why simply averaging more trials would not solve it.

SUMMARY

Lesson Summary

Designing a strong investigation in IB Chemistry begins with a focused, testable research question that clearly specifies the independent variable (what you change) and the dependent variable (what you measure). A scientifically justified hypothesis predicts the expected relationship. Keeping all controlled variables constant ensures a fair test, while using at least five IV values and five repeated trials per value produces reliable data with minimized random error.

When evaluating your design, distinguish between random errors (which reduce precision and are reduced by more trials) and systematic errors (which reduce accuracy and must be corrected at their source). Choose apparatus that minimizes percentage uncertainty, consider safety and ethical aspects, and write a method detailed enough for another student to reproduce your work. These skills form the foundation of the IB Internal Assessment and all future scientific inquiry.