Collecting six hundred readings is easy. Turning them into something that answers your research question is the part that earns marks. A spreadsheet does the arithmetic; you still have to decide what the arithmetic should be.
📚 What you need to know
Spreadsheets do three jobs: organise, manipulate and visualise data.
Raw data goes in columns and rows; calculated values go in their own column, using a formula copied down.
Graphs are made from the processed data, not the raw data, unless the raw data is already what you want to plot.
Line and scatter graphs show trends; bar charts compare categories; pie charts show parts of a whole.
A calibration curve plots a measured signal against known concentrations, so an unknown can be read off it.
Computer modelling and 3D visualisation help you see structures and reaction pathways.
Technology speeds up the processing; it does not decide what is worth processing.
What “processing” actually means
Raw data is whatever came out of the instrument. Processed data is what you calculated from it: an average, a difference, a rate, a concentration, an enthalpy change.
An internal assessment that shows only raw data and a graph of raw data will not score well. The examiner wants to see you take the numbers somewhere.
Keep raw and processed data in separate columns and label both with units. It makes the working obvious without you having to explain it.
The three jobs a spreadsheet does
Organisation. Raw readings go into labelled columns and rows, sorted into whatever order makes sense. Nothing is lost and nothing is copied out twice.
Manipulation. Calculations, averages, standard deviations and unit conversions all happen with one formula, copied down the column. If you spot a mistake in the formula, you fix it once.
Visualisation. Built-in chart tools turn a column of numbers into a graph in a couple of clicks, so trends and outliers show up straight away.
The real advantage of a spreadsheet is not speed, it is that the working is reproducible. Change one raw reading and every calculated value and every graph updates. Doing it by hand, you would have to start again.
Picking the right kind of graph
Choosing the wrong chart type is a surprisingly common way to lose a mark. The question to ask is what the graph is meant to show.
Almost every graph in an IB Chemistry internal assessment is a scatter graph with a line of best fit. The other two are rare, so think carefully before using them.
Chart type
Use it when
Chemistry example
Scatter with best-fit line
Both variables are continuous
Absorbance against concentration
Line graph
Following one quantity over time
pH against volume of acid added
Bar chart
Comparing separate categories
First ionisation energy of Period 3 elements
Pie chart
Showing proportions of one total
Composition of a mixture by mass
Calibration curves
A colorimeter measures how much light a coloured solution absorbs. That absorbance depends on concentration, but the instrument does not know the relationship — you have to show it.
So you make up several standard solutions of known concentration, measure the absorbance of each, and plot absorbance against concentration. The line you get is the calibration curve. Any unknown of the same substance can then be read straight off it.
The gradient of this line is 0.860 per mol dm–3, so 0.470 ÷ 0.860 gives 0.546 mol dm–3. Reading it off the graph and calculating it should agree.
Only read inside your standards. The line was tested between 0 and 1.0 mol dm–3. Extending it out to 2.0 and reading a value off is guessing, because you have no evidence the relationship stays straight out there.
Modelling and 3D visualisation
Computational models let chemists explore processes that would take far too long or cost far too much to investigate experimentally. They are a tool for narrowing down the options before anyone picks up a flask.
The version you are most likely to use is 3D structure visualisation. MolView is a free tool where you draw a structure in two dimensions and immediately see it rotating in three, which makes bond angles and molecular shape far easier to grasp than any flat diagram.
Visualisation is also used for:
Reaction pathway diagrams, which show how the energy changes as a reaction proceeds and help explain mechanisms.
Kinetic data plots, which turn columns of concentration and time into an order of reaction.
Molecular orbital and electron density pictures, which show where charge sits in a molecule.
The same warning applies as ever: a model is a simplification. It is a good way to build understanding and a bad way to prove something.
🧩 Turning raw readings into a finished graph
Put raw data in its own columns, each with a heading and a unit.
Add a processed column and write the formula once, then copy it down.
Check one value by hand. If the spreadsheet and your calculator disagree, the formula is wrong.
Plot the processed data with the independent variable on the x-axis.
Label both axes with quantity and unit, and add a best-fit line if the relationship is meant to be linear.
Look for outliers before you draw conclusions, and say what you did about any you found.
Worked examples
WORKED EXAMPLE
Using the calibration curve above, a solution of unknown concentration gives a relative absorbance of 0.300. Find its concentration and state one assumption you are making.
Find the gradient of the line
The line passes through the origin and through (1.00, 0.858).
gradient = 0.858 ÷ 1.00 = 0.860 per mol dm–3Rearrange for concentrationc = absorbance ÷ gradientSubstitutec = 0.300 ÷ 0.860 = 0.3488…c = 0.349 mol dm–3 (3 s.f.)assumption: the unknown was measured on the same colorimeter, same wavelength, same cuvette
WORKED EXAMPLE
A student has logged temperature every 0.5 s for 300 s during a neutralisation. Describe how a spreadsheet would be used to find the maximum temperature rise, and say why a graph is still needed.
Organise the data
Two columns: time in seconds, temperature in °C. The logger fills them automatically.
Manipulate
Add a column for temperature rise, using the starting temperature as the reference, copied down all 600 rows.
Why a graph is still needed
The highest logged value is not the true maximum, because the mixture starts cooling while it is still reacting.
What the graph gives you
Plot temperature against time, extrapolate the cooling line back to the moment of mixing, and read the corrected rise there.
The spreadsheet does the arithmetic; the graph corrects for heat lossthis is why “just take the biggest number” underestimates every calorimetry result
💡 Exam tip
Show raw and processed data separately in your internal assessment. Both are credited.
Every column and every axis needs a quantity and a unit. This is one of the easiest marks in the whole course.
Put the independent variable on the x-axis, without exception.
Use a line of best fit, not a dot-to-dot. Joining points with straight segments implies data you do not have.
Only read values inside the range your calibration covered.
State the uncertainty of the instrument somewhere. A logger reduces uncertainty; it does not remove it.
⚠️ Common mix-up
Plotting raw data and calling it processing. Processing means calculating something new from the raw values.
Using a bar chart for continuous data. Concentration and time are continuous, so use a scatter graph.
Joining every point with straight lines. Draw a best-fit line or curve instead.
Extrapolating a calibration curve far beyond the standards. The relationship may not hold out there.
Trusting a spreadsheet formula you never checked. Always verify one row by hand.
Quoting every digit the spreadsheet gives. Round to match the precision of your measurements.
That completes Tool 2. Collecting data well and processing it honestly are two halves of the same skill, and between them they account for a very large share of the internal assessment marks.
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