Analyzing Data for High Achievement

Which type of data must be sufficient in volume and directly relevant to the research question (RQ)?

• Quantitative data

• Qualitative data

• Processed data

• Uncertainty data

What should be included in the presentation of raw data to ensure clarity?

• Only the final results

• Units and uncertainties/precision if relevant

• Only graphical representations

• Only averages

Which of the following is a common pitfall that can lead to lower scores?

• Data is complete and well-processed

• Uncertainty is ignored or poorly integrated

• Interpretation is detailed and relevant

• Sufficient quantitative data is provided

What is the purpose of processing data (such as calculating averages or creating graphs) in a scientific investigation?

• To make the data look more impressive

• To fully support a valid conclusion

• To reduce the amount of data collected

• To avoid discussing uncertainties

If a student includes error bars and discusses percentage errors in their analysis, which aspect of high achievement are they addressing?

• Raw Data

• Processed Data

• Uncertainty

• Interpretation

A student collects data using a digital sensor and presents it in a graph. What must they ensure to meet high achievement standards?

• Only show the graph without any explanation

• Include units and indicate uncertainties/precision if relevant

• Ignore any measurement errors

• Only present qualitative data

A student’s conclusion is not directly related to the research question and does not use processed results or uncertainty. Which aspect of high achievement is lacking?

• Raw Data

• Processed Data

• Uncertainty

• Interpretation

Given a set of measurements with known uncertainties, describe how you would incorporate uncertainty into your analysis to achieve a high score.

• Ignore the uncertainties and only use the average value

• Discuss error bars, uncertainty ranges, and propagate errors in calculations

• Only mention uncertainties in the conclusion

• Use only qualitative descriptions

A student has processed their data by calculating averages and creating graphs, but their conclusion does not match the data trends. What should the student do to improve their interpretation?

• Change the data to fit the conclusion

• Re-examine the processed data and uncertainties to draw a conclusion directly relevant to the RQ

• Ignore the mismatch and submit the report

• Only focus on qualitative data

You notice that your data set is missing some measurements and your calculations have inconsistencies. What steps should you take to avoid common pitfalls and achieve a higher score?

• Submit the report as is

• Justify the missing data and correct calculation errors before finalizing your analysis

• Only focus on the interpretation section

• Ignore the inconsistencies