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Learn: Interpreting Risk - Correlation, Causation, and Health Data
Edexcel A Level Biology SNAB A
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Welcome!You've already studied topics like cardiovascular health, energy budgets, and BMI. Now, let's build on those foundations to explore how scientists interpret risk using health data.
What is Risk?Risk is the likelihood of harm or disease occurring as a result of exposure to certain factors. Understanding risk helps scientists and doctors make decisions about health and treatment.
Correlation vs. CausationCorrelation means two variables are linked (e.g., smoking rates and lung cancer cases). However, correlation does not prove causation, which is when one variable directly causes the other.
Quick check: What does correlation mean?
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Evaluating Health DataHealth data often includes information like disease rates, lifestyle factors, and population statistics. Scientists use this data to identify patterns and assess risks.
Relative RiskRelative risk compares the likelihood of an outcome between two groups. For example, smokers might have a 3x higher chance of developing lung cancer compared to non-smokers.
Which of the following statements about relative risk are true? (Select all that apply)
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How Bias Affects DataBias occurs when data or study methods favour certain outcomes, leading to inaccurate conclusions. Scientists work to minimise bias by using large samples and randomised studies.
Match the items on the left with their correct pairs on the right
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Review Time!Great work! You've learned about risk, correlation, causation, and evaluating health data. Now let's test your understanding with a few questions.
Which of the following proves causation?
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Bias occurs when data collection methods favour certain {{blank0}}, leading to inaccurate conclusions.
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Which methods can minimise bias in studies? (Select all that apply)
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Final ThoughtsUnderstanding risk, correlation, and causation helps us interpret health data accurately. These concepts are essential for assessing the impact of lifestyle choices and treatments.
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