Practical skills Flashcards
What is the first step when planning an experiment?
The first step is to identify the research question or hypothesis, determining what you are trying to investigate or test.
Why is it important to control variables in an experiment?
Controlling variables ensures that only the independent variable is affecting the dependent variable, allowing for valid and reliable results.
What is the difference between independent and dependent variables?
The independent variable is what you change or manipulate in the experiment, while the dependent variable is what you measure or observe as a result of the changes.
Why is it necessary to have a control group in an experiment?
A control group serves as a baseline for comparison, showing what would happen without the experimental treatment or manipulation.
What are replicates in an experiment?
Replicates are repeated measurements or tests to ensure the results are reliable and to account for random variability in the data.
How do you process data in an experiment?
Data can be processed by organizing, calculating averages, creating graphs or tables, and performing statistical analysis to identify trends or relationships.
What is the purpose of creating graphs or charts with experimental data?
Graphs and charts visually present data, making it easier to interpret trends, patterns, or relationships between variables.
What types of graphs are suitable for presenting categorical data?
Bar graphs or pie charts are commonly used to display categorical data.
When should you use a line graph in a data presentation?
A line graph is used when showing the relationship between two continuous variables, especially to display trends over time.
What are error bars in a graph?
Error bars represent the variability or uncertainty in the data, showing the range of possible values or the precision of measurements.
What does it mean to draw a conclusion from experimental data?
Drawing a conclusion involves interpreting the data to determine whether the hypothesis is supported or refuted, considering the patterns or relationships observed in the results.
How can you ensure your conclusion is valid?
Ensure the conclusion is based on sufficient data, the experiment was well-controlled, and the results consistently support the hypothesis.
Why should you consider alternative explanations when drawing conclusions?
Considering alternative explanations helps ensure that the conclusion is robust and not biased by unaccounted variables or uncontrolled factors.
What is the importance of discussing statistical significance when drawing conclusions?
Statistical significance indicates whether the results are likely due to the independent variable or if they occurred by chance.
Why is it important to evaluate an experiment after conducting it?
Evaluating the experiment helps identify sources of error, assess reliability and validity, and suggest improvements for future investigations.
What should be considered when evaluating the validity of an experiment?
The validity is determined by how well the experiment tests the hypothesis, whether it controls for confounding variables, and whether the results are reproducible.
How can you identify sources of error in an experiment?
Sources of error can be identified by reviewing experimental methods, considering measurement precision, and assessing external factors that might have influenced the results.
What is the difference between systematic errors and random errors?
Systematic errors are consistent and reproducible inaccuracies due to faults in equipment or experimental design, while random errors are unpredictable fluctuations in data due to factors like measurement limitations.
What is reliability in the context of an experiment?
Reliability refers to the consistency of the results when the experiment is repeated, or when measurements are taken multiple times under the same conditions.
How can you improve the reliability of an experiment?
Increasing the number of replicates, ensuring precise measurements, and using consistent procedures can improve the reliability of an experiment.
Independent Variable
The variable that is manipulated or changed by the experimenter to observe its effect on the dependent variable.
Dependent Variable:
The variable that is measured or observed in response to changes in the independent variable.
Control Group
A group in an experiment that is not exposed to the independent variable, used for comparison with the experimental group.
Replicates
Repeated measurements or trials that help to ensure the reliability and accuracy of the results.
Graph
A visual representation of data, often used to illustrate relationships between variables in an experiment.
Line Graph
A graph that displays continuous data, often showing trends or relationships over time.
Error Bars
Indicators on a graph that represent the variability or uncertainty in the data.
Conclusion
A statement that summarises the findings of an experiment, explaining whether the hypothesis is supported by the data.
Alternative Explanation
A consideration of other possible factors or interpretations that could account for the results observed in an experiment.
Statistical Significance
A measure of whether the results observed in an experiment are likely to be due to the independent variable rather than random chance.
Validity
The extent to which an experiment measures what it intends to measure and produces accurate results.
Sources of Error
Factors that can lead to inaccuracies or inconsistencies in the results of an experiment.
Systematic Error
A consistent and repeatable error that occurs due to faulty equipment, procedures, or biases in the experiment.
Random Error
An unpredictable error that arises from factors like human error or limitations of measurement tools.
Reliability
The consistency of experimental results when repeated or when different observers perform the experiment.
Improvement
Suggestions for modifying the experimental design or methodology to reduce errors or increase reliability and validity in future experiments.
