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What is the definition of Random Patterns?
Data variations that are not systematic and unpredictable, often due to random error.
What is the definition of Non-Random Patterns?
Systematic variations in data that can be predicted to some degree, often associated with bias.
What is bias in statistics?
A systematic error that can lead to non-random patterns in data.
Define statistical significance.
The likelihood that a result is not due to chance alone.
What is random assignment?
Assigning participants to different groups randomly to ensure differences are due to the treatment, not other factors.
What are the differences between random and non-random patterns?
Random Patterns: Unpredictable, not systematic | Non-Random Patterns: Predictable, systematic
What are the differences between random patterns and bias?
Random Patterns: Due to chance, unpredictable | Bias: Systematic error, predictable influence
What are the differences between correlation and causation?
Correlation: Association between variables | Causation: One variable directly causes a change in another
What are the differences between a treatment group and a control group in a study?
Treatment Group: Receives the treatment being tested | Control Group: Does not receive the treatment, often receives a placebo
What are the differences between statistical significance and practical significance?
Statistical Significance: Result unlikely due to chance | Practical Significance: Result has real-world importance/impact
Explain the concept of random patterns.
Random patterns occur when data variations are unpredictable and not systematic, like coin flips or variations in student heights.
Explain the concept of non-random patterns.
Non-random patterns show systematic and predictable variations, like the relationship between education and income or age and heart disease.
Explain how random assignment minimizes bias.
By randomly assigning participants to groups, any pre-existing differences are evenly distributed, reducing the chance that these differences will skew the results.
Explain why patterns don't automatically mean data is unbiased.
Even if a pattern is observed, random variation and error can still be present, potentially influencing the data and leading to incorrect conclusions.
Explain the importance of considering potential sources of bias when analyzing data.
Identifying potential biases is crucial for drawing valid conclusions, as bias can create non-random patterns that distort the true relationship between variables.