Practical Biology (HT 1 3rd year)

What should you consider when planning an experiment?

The aim, hypothesis, variables, equipment, method, safety, and how to collect results.

What are the key principles of good experimental technique?

Follow a clear method, use equipment correctly, measure carefully, control variables, and repeat measurements where appropriate.

What is an independent variable?

The variable that you change.

What is a dependent variable?

The variable that you measure.

What are control variables?

Variables that are kept the same throughout an experiment.

Why are control variables important?

They make it a fair test so that changes in the dependent variable are due to the independent variable.

What is the purpose of a control experiment?

To provide a comparison to help determine whether the independent variable causes an effect.

Why is accurate data important?

It helps ensure that results are close to the true values.

Why is precise data important?

It means measurements are recorded consistently and to an appropriate level of detail.

Why is reliable data important?

It means repeated measurements or experiments give similar results.

How can you collect accurate data?

Use suitable equipment, read scales correctly, and measure carefully.

How can you collect precise data?

Use equipment with suitable resolution and record measurements consistently.

How can you collect reliable data?

Repeat measurements or experiments and compare the results.

What should a well-controlled, accurate and reliable experimental method include?

A clear step-by-step procedure, identified variables, control of relevant variables, suitable equipment, careful measurements, and repeats.

What is the purpose of a hypothesis?

To make a testable prediction about what will happen in an experiment.

What are the key components of a good data table?

Clear headings, units, and results recorded in the correct columns.

How should units be included in a data table?

In the column headings.

What are the different graphical methods for presenting data?

Line graphs, bar charts and scatter graphs.

When should you use a line graph?

To show how one continuous variable changes with another.

When should you use a bar chart?

To compare results between different categories.

When should you use a scatter graph?

To show the relationship between two variables.

How do you choose the most appropriate graph?

Consider the type of data and whether you are showing a trend, relationship, or comparison between categories.

What are the key elements of a good conclusion?

Answer the aim, describe the pattern in the results, and use data to support the conclusion.

How can you analyse data to produce a good conclusion?

Identify patterns and trends, compare results, and use numerical evidence.

How do you write a conclusion from experimental results?

State what the results show and support your statement with evidence from the data.

What should you consider when evaluating an experiment?

The quality of the method and data, possible errors, limitations, and improvements.

How can experimental data be evaluated for accuracy?

Consider how close the measurements are to the true values.

How can experimental data be evaluated for reliability?

Check whether repeated measurements or experiments give similar results.

How can an experimental method be evaluated for validity?

Check whether it is a fair test and whether it investigates what it was intended to test.