What are the differences between correlation and causation?
Correlation: Measures the strength and direction of a linear relationship. Causation: Indicates that one event is the result of the occurrence of the other event. | Correlation: Does not imply causation. Causation: Requires a direct relationship and control of confounding variables.
What are the differences between random error and systematic error?
Random Error: Unpredictable, affects measurements inconsistently. Systematic Error: Predictable, affects measurements consistently in the same direction. | Random Error: Cannot be eliminated but can be reduced by averaging multiple measurements. Systematic Error: Can be identified and corrected by calibrating instruments or refining methods.
Differentiate between correlation and confounding variables.
Correlation: A statistical relationship between two variables. Confounding Variables: A third variable that influences both the independent and dependent variables, obscuring the true relationship. | Correlation: Can be positive, negative, or zero. Confounding Variables: Can create a spurious correlation or mask a true relationship.
Compare and contrast random sampling vs non-random sampling.
Random Sampling: Each member of the population has an equal chance of being selected. Non-Random Sampling: Some members of the population are more likely to be selected than others. | Random Sampling: Minimizes bias and allows for generalization to the population. Non-Random Sampling: Can introduce bias and limit the generalizability of the results.
What are the differences between experimental and observational studies?
Experimental Studies: Researchers manipulate one or more variables to determine the effect on another variable. Observational Studies: Researchers observe and measure variables without manipulating them. | Experimental Studies: Can establish cause-and-effect relationships. Observational Studies: Can only identify associations or correlations.
Explain the concept of correlation not equaling causation.
Just because two variables are related does not mean one causes the other. A third variable or random chance could be the reason for the relationship.
Explain the importance of repetition in statistical studies.
Repeating a study with large sample sizes in multiple populations helps ensure the results are valid and not due to random chance.
Explain the impact of large sample sizes on study validity.
Larger sample sizes provide more reliable results, reducing the likelihood that observed effects are due to random chance.
Explain how confounding variables can mislead study results.
Confounding variables can create a false sense of cause and effect by influencing both the independent and dependent variables.
Explain the difference between random and systematic error.
Random error is unpredictable and varies, while systematic error is consistent and biased, affecting all measurements in a similar way.
What is correlation?
A statistical measure that expresses the extent to which two variables are linearly related, meaning they change together at a constant rate.
What is causation?
A relationship where one variable directly influences another, leading to a predictable outcome.
Define confounding variable.
A variable that influences both the independent and dependent variables, creating a spurious association.
What is random error?
Unpredictable and uncontrollable variation in data due to chance.
What is systematic error?
Predictable and consistent bias in measurements due to a flaw in the method or instrument.
