Critically analyse how different presentation methods could affect the interpretation of findings from an investigation comparing the effects of continuous versus interval aerobic training on VO₂ max improvement. (6 marks)
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Sample Answer
- Bar graphs showing average improvements could visually emphasise differences between training methods, but without error bars might exaggerate small differences that aren’t statistically significant.
- Scatter plots showing individual data points would transparently reveal the distribution of responses in both groups, preventing misinterpretation of averages that might mask substantial overlap between methods.
- Line graphs tracking changes over time would effectively show the rate of improvement for each method, revealing whether one method produces faster initial gains versus greater long-term improvements.
- Line graphs tracking changes over time would effectively show the rate of improvement for each method, revealing whether one method produces faster initial gains versus greater long-term improvements.
- Using percentage change rather than absolute values could alter interpretation by normalising improvements relative to baseline, potentially favoring the method that works better for initially less-fit individuals.
- Selectively highlighting specific time points or variables where differences are greatest could create bias toward one method, whereas comprehensive data presentation with effect sizes would provide a more balanced interpretation of relative effectiveness.
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Sample Answer
- Bar graphs showing average improvements could visually emphasise differences between training methods, but without error bars might exaggerate small differences that aren’t statistically significant.
- Scatter plots showing individual data points would transparently reveal the distribution of responses in both groups, preventing misinterpretation of averages that might mask substantial overlap between methods.
- Line graphs tracking changes over time would effectively show the rate of improvement for each method, revealing whether one method produces faster initial gains versus greater long-term improvements.
- Line graphs tracking changes over time would effectively show the rate of improvement for each method, revealing whether one method produces faster initial gains versus greater long-term improvements.
- Using percentage change rather than absolute values could alter interpretation by normalising improvements relative to baseline, potentially favoring the method that works better for initially less-fit individuals.
- Selectively highlighting specific time points or variables where differences are greatest could create bias toward one method, whereas comprehensive data presentation with effect sizes would provide a more balanced interpretation of relative effectiveness.