Paper 1: multiple choice and data-based questions
How to approach Paper 1A multiple-choice questions and Paper 1B data-based questions, with worked questions and markschemes.
- IB Biology
- Exam skills
- SL and HL
Paper 1 has two parts taken in one sitting. Paper 1A is multiple choice: each question has four options and one correct answer. Paper 1B is a set of data-based questions on experimental work, built on results from investigations you have not seen before. The two parts test different skills, so they need different approaches.
At SL, Paper 1 lasts 1 hour 30 minutes: Paper 1A has 30 multiple-choice questions (30 marks) and Paper 1B has data-based questions worth 25 marks, 55 marks in all. At HL, Paper 1 lasts 2 hours: Paper 1A has 40 questions (40 marks) and Paper 1B is worth 35 marks, 75 marks in all. Paper 1 carries 36% of the final grade at both levels.
Because the two parts share one sitting with no break, time saved on Paper 1A can be spent on Paper 1B, where questions are longer and carry more marks each.
Paper 1A: multiple-choice questions
Paper 1A covers the whole course. Most questions test recall and understanding of a single idea; some ask you to read a diagram or micrograph, interpret a short data set or carry out a quick calculation such as magnification or percentage change. There is no penalty for a wrong answer, so every question should be answered.
A method for every multiple-choice question
Read the stem and cover the options. Decide what the answer should be before looking.
Read all four options, even if the first one matches your answer. A later option may be more complete or more precise.
Eliminate the options you know are wrong, and say to yourself why each is wrong.
Check the stem again for words that change its meaning: NOT, LEAST, MOST, ALWAYS, ONLY.
For a calculation, work it out on paper first; the distractors are usually the results of common slips.
If you cannot decide, choose between the remaining options and mark the question to return to; never leave it blank.
Each distractor is built on a specific error. Recognising the type helps you eliminate it quickly.
Type of distractor | How it works | How to spot it |
|---|---|---|
A common misconception | States an idea many students believe, such as "mitochondria produce energy" or "plants respire only at night". | Ask whether the statement would survive a careful textbook definition. |
True but irrelevant | A correct statement that does not answer the question asked. | Reread the stem: does the option answer this question, or a different one? |
Partly correct | Right process but wrong detail: the right enzyme with the wrong bond, the right organelle with the wrong membrane number. | Check every word of the option, not just the key term. |
A calculation slip | The number you get by dividing the wrong way, forgetting a unit conversion or using the wrong denominator. | Work the calculation yourself and check units at each step. |
Reversed cause and effect | Water is polar because it forms hydrogen bonds, instead of the other way round. | Ask which comes first in the mechanism. |
Worked multiple-choice questions
Each wrong option in that question names a real property of water but states it incorrectly, which is why every word of an option needs checking.
Calculation questions in Paper 1A are short, but the distractors are chosen to match the most common slips, so a careless answer is almost always among the options.
Options B and D differ by a single word. Questions like this test the detail of a process as well as recognition of the enzyme involved.
Paper 1B: data-based questions on experimental skills
Each Paper 1B question describes an investigation and gives its results as a table, graph, micrograph, gel or statistical output. The questions then test the skills of experimental work: identifying variables, processing data, reading graphs, evaluating the method and drawing conclusions. The biology is from the course, but the context is usually new, so answers must refer to the experiment given.
Typical stem | What it asks you to do |
|---|---|
Identify the independent variable. | Name the variable the investigator changed, as precisely as possible ("concentration of sucrose solution", not "sucrose"). |
State the trend in … between day 0 and day 10. | Direction of change, with values at the start and end and units. |
Calculate the mean rate / percentage change. Show your working. | Formula, substitution, answer with unit. |
Deduce whether the data support the hypothesis that … | Yes, no or partly, with the specific data that show it. |
Suggest one reason for the large error bars at 40 °C. | A cause specific to this method or organism, not "human error". |
Evaluate the method used to measure … | At least one strength and one limitation, each tied to the method, and a judgement. |
Outline how the experiment could be modified to … | A specific, practical change, with what would be measured or controlled. |
State the null hypothesis for this test. / Explain what the p-value indicates. | The "no difference / no association" statement; whether p is below 0.05 and what that means. |
Statistics, error bars and the full set of calculations are worked through in Data, graphs and statistics. Experimental methods such as colorimetry, chromatography and sampling are in Practical techniques and biological drawing.
Reading graphs and tables
Before answering any question on a graph, read its title, both axis labels with their units, the scale of each axis and any key. Many lost marks come from reading the wrong curve or missing that an axis does not start at zero, is logarithmic, or that a second y-axis is on the right.
Quote values with units: "the rate rose from 2.0 to 8.5 cm³ min⁻¹".
Refer to the independent variable when you give a trend: "as temperature increased from 10 to 40 °C".
Name turning points, plateaus and the range over which a trend holds.
Distinguish a rate (per unit time) from a total.
Read error bars: wide bars mean variable repeats; overlapping standard deviation bars suggest the difference may not be significant.
The graph of germination against salt concentration is typical of a Paper 1B figure: a line through a small number of means, with the independent variable on the x-axis.
- Germination
A full Paper 1B question with a data table
A student investigated the effect of temperature on the activity of catalase in potato extract. Equal volumes of extract and hydrogen peroxide solution were mixed in a water bath at each temperature, and the volume of oxygen produced in 60 s was collected in a gas syringe. The results, a practice data set, are shown in the table.
Temperature / °C | Trial 1 / cm³ | Trial 2 / cm³ | Trial 3 / cm³ | Mean / cm³ |
|---|---|---|---|---|
10 | 4.0 | 4.4 | 4.2 | 4.2 |
20 | 7.8 | 8.2 | 8.3 | 8.1 |
30 | 12.1 | 11.6 | 12.4 | 12.0 |
40 | 14.0 | 9.2 | 14.6 | ? |
50 | 6.1 | 5.8 | 6.4 | 6.1 |
Evaluating methods and suggesting improvements
Evaluation questions reward limitations that are specific to the method described, with their effect on the results, and improvements that address them. Generic answers such as "human error", "be more careful" or "do more repeats" score nothing on their own.
Specific limitation | Effect on the results | Realistic improvement |
|---|---|---|
Oxygen escapes before the bung is fitted | Volumes are too low; the error is larger at high rates | Add the enzyme through a sealed port / use a pressure sensor |
Temperature of the mixture not checked | True temperature may differ from the bath, especially at 10 and 50 °C | Pre-incubate both solutions to the bath temperature and check with a thermometer |
Only three trials with one large anomaly | Mean at 40 °C uncertain; optimum cannot be located precisely | Five trials per temperature; extra temperatures between 35 and 45 °C |
Potato extract from different tubers | Catalase concentration may differ between trials | Prepare one batch of extract and use it for all trials |
A strong evaluation states which limitation matters most. Here, the lost oxygen is a systematic error that affects every result in the same direction, while the anomaly is a random error that affects one value.
Hypotheses, conclusions and statistics
Paper 1B often gives the output of a statistical test and asks what it shows. A null hypothesis states that there is no difference or no association. If the p-value is less than 0.05, the result is statistically significant and the null hypothesis is rejected; if it is greater than 0.05, the null hypothesis is not rejected.
YouTubeManaging the sitting
Paper 1A rewards speed and Paper 1B rewards care. A sensible plan is to work through Paper 1A at a steady pace, mark questions to come back to, and move on to Paper 1B with time in hand, returning to the marked multiple-choice questions at the end. Paper 1B questions are multi-part and each part is short, so leaving one part blank to finish the others is better than running out of time with a whole question untouched.
Read the introduction to each Paper 1B question fully before answering: it says what was measured and how.
Annotate the data: circle anomalies, mark the range of a trend, underline units.
Answer in the context given: name the organism, the variable and the values.
Check that each calculated answer has a unit and sensible precision.
Carry on with Biology in Augment
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