Correlation is not Causation

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  • “The phrase ‘correlation is not causation’ is a fundamental concept in statistics and scientific research, emphasizing that a relationship between two variables does not automatically imply that one causes the other.”
  • “Correlation refers to a statistical measure that describes the size and direction of a relationship between two or more variables. A positive correlation indicates that both variables tend to increase together, while a negative correlation indicates that as one variable increases, the other decreases.”
  • “Causation, or causal relationship, indicates that one event is the result of the occurrence of the other event; i.e., there is a cause-effect relationship between variables.”
  • “The key reason why correlation does not imply causation is that the observed relationship might be coincidental or influenced by other factors, known as confounding variables.”

Table of Contents: (Click any link below to navigate to that section.)

  1. Elaborate on the notion that “correlation is not causation”.
  2. Take the question of whether a good night’s sleep is correlated with or causes better scores on tests taken the next day. Design 2 experiments. The first designed to show merely a correlation, and the second designed to show causation.
    1. Experiment 1: Demonstrating Correlation
    2. Experiment 2: Demonstrating Causation
    3. Experiment 1: Demonstrating Correlation
    4. Experiment 2: Establishing Causation
  3. Elaborate in detail the general principles and practices necessary when one wishes to go beyond mere correlation to show actual causation.
    1. 1. Establishing Temporal Precedence
    2. 2. Controlling for Confounding Variables
    3. 3. Establishing a Causal Mechanism
    4. 4. Using Strong Research Designs
    5. 5. Ruling Out Alternative Explanations
    6. 6. Generalization of Findings
  4. Create a 10-item quiz on the entire thread above.
    1. Quiz: Understanding Correlation and Causation
    2. Answers
  5. Provide 15 discussion questions relevant to the content above.

Elaborate on the notion that “correlation is not causation”.

The phrase “correlation is not causation” is a fundamental concept in statistics and scientific research, emphasizing that a relationship between two variables does not automatically imply that one causes the other. This distinction is critical for correctly interpreting data and making informed decisions based on research findings. Here’s a more detailed breakdown:

  1. Definition of Correlation:
    • Correlation refers to a statistical measure (expressed as a correlation coefficient) that describes the size and direction of a relationship between two or more variables. A positive correlation indicates that both variables tend to increase together, while a negative correlation indicates that as one variable increases, the other decreases.
  2. Definition of Causation:
    • Causation, or causal relationship, indicates that one event is the result of the occurrence of the other event; i.e., there is a cause-effect relationship between variables. Establishing causation means proving that changes in one variable bring about changes in another.
  3. Why Correlation Does Not Imply Causation:
    • The key reason why correlation does not imply causation is that the observed relationship might be coincidental or influenced by other factors, known as confounding variables.
    • Examples:
      • Ice Cream Sales and Shark Attacks: There is a positive correlation between ice cream sales and shark attacks, but higher ice cream sales do not cause shark attacks. Both are likely influenced by a third factor, such as warmer weather.
      • Bed Size and Sleep Quality: Studies might find a correlation between larger bed sizes and better sleep quality. However, this doesn’t mean the bed size causes better sleep; potentially, wealthier individuals who can afford bigger beds might also invest in better sleep environments.
  4. Identifying Causal Relationships:
    • To determine causation, researchers need to conduct experiments that control for other variables, or they must use statistical models to account for potential confounders.
    • Randomized Controlled Trials (RCTs) are considered the gold standard for determining causation. By randomly assigning participants to different groups, researchers can more reliably infer causal effects of the intervention being studied.
  5. Importance in Research and Policy:
    • Understanding that correlation does not imply causation is crucial for interpreting studies and data correctly. Mistaking correlation for causation can lead to faulty policies, health recommendations, and business decisions.
    • It encourages a more thorough investigation into the underlying mechanisms and potential confounders in any observed relationship.

In summary, while correlation can suggest that there is a relationship worth further investigation, it must not be used alone to infer causative relationships without additional evidence or experimental data that supports such a link. This understanding is essential for anyone involved in research, analytics, policy-making, and general critical thinking.

The phrase “correlation does not imply causation” is an important principle in statistics and scientific research. It serves as a reminder that just because two variables are correlated (meaning they tend to change or occur together), it does not necessarily mean that one variable is causing the other.

Correlation simply means that there is an observed relationship or pattern between two variables. However, this relationship could arise for various reasons, and it does not automatically imply a direct causal link. There could be other confounding factors or variables involved that account for the observed correlation.

For example, imagine that there is a correlation between ice cream sales and the number of drowning incidents at a particular beach. It would be incorrect to conclude that eating ice cream causes people to drown or that drowning causes people to eat more ice cream. In this case, the correlation likely arises because of a third variable, such as hot weather, which increases both ice cream consumption and the number of people swimming (and thus the risk of drowning).

Establishing causation requires more rigorous investigation and evidence beyond just a correlation. Researchers often rely on controlled experiments, longitudinal studies, or other methods to account for potential confounding factors and rule out alternative explanations.

It’s important to understand that correlation is not the same as causation because making faulty causal assumptions can lead to incorrect conclusions, ineffective interventions, or even harmful consequences. The “correlation does not imply causation” principle reminds researchers and decision-makers to exercise caution when interpreting correlational data and to seek further evidence before making causal claims.

Take the question of whether a good night’s sleep is correlated with or causes better scores on tests taken the next day. Design 2 experiments. The first designed to show merely a correlation, and the second designed to show causation.

To explore the relationship between a good night’s sleep and test scores, we can design two different experiments: one to demonstrate correlation and another to establish causation. Here’s how each can be structured:

Experiment 1: Demonstrating Correlation

Objective: To find if there is a correlation between the amount of sleep students get and their scores on tests taken the next day.

Design:

  1. Participants:
    • Recruit a large group of students from various backgrounds and educational levels.
  2. Procedure:
    • Ask participants to report their average hours of sleep per night over a week. This self-reported data should include at least one school night before a test day.
    • Collect data on the participants’ test scores from tests taken the day following their recorded sleep.
  3. Data Collection:
    • Sleep data: Participants log their sleep hours each night using a diary or a sleep-tracking app.
    • Test scores: Collect scores from tests taken after the sleep tracking period.
  4. Statistical Analysis:
    • Calculate the correlation coefficient (e.g., Pearson’s r) to measure the strength and direction of the relationship between sleep duration and test scores.

Outcome:

  • This study will reveal whether there is a statistically significant correlation between sleep length and test scores. However, it cannot establish if changes in sleep directly cause changes in test performance, as other factors (study habits, nutrition, stress) might influence both variables.

Experiment 2: Demonstrating Causation

Objective: To determine if altering sleep duration causes a change in test scores.

Design:

  1. Participants:
    • Randomly select a group of students from a similar demographic and educational background.
  2. Random Assignment:
    • Randomly assign participants to two groups: a control group and an experimental group.
  3. Intervention:
    • Control Group: Maintain normal sleeping habits.
    • Experimental Group: Introduce an intervention where these students are required to sleep for a prescribed duration (e.g., 8 hours) which is different from their normal sleep pattern.
  4. Procedure:
    • Over a week, ensure that the experimental group adheres to the sleep intervention using sleep tracking devices. The control group continues with their regular sleep pattern.
    • At the end of the week, administer a standardized test to all participants.
  5. Data Collection:
    • Sleep data: Monitored and verified through wearable sleep trackers.
    • Test scores: Administer the same test to both groups at the end of the intervention period.
  6. Statistical Analysis:
    • Use statistical tests (e.g., ANOVA) to compare the test scores between the two groups, controlling for any confounding variables like previous test scores, baseline academic performance, etc.

Outcome:

  • This controlled experiment can more reliably demonstrate causation, as it isolates sleep as the independent variable while controlling other factors. If the experimental group’s test scores significantly improve relative to the control group, one might conclude that increased sleep directly improves test performance.

These experiments highlight the different setups required to explore correlation versus causation. The randomized controlled trial (Experiment 2) is particularly crucial for testing causal hypotheses in a scientifically rigorous manner.

To investigate the relationship between sleep and test performance, we can design two experiments: one to demonstrate correlation and another to establish causation.

Experiment 1: Demonstrating Correlation

In this experiment, we aim to observe the correlation between sleep duration and test scores without manipulating any variables.

Procedure:

  1. Recruit a sample of participants from a population of interest (e.g., high school students, college students, or employees).
  2. Ask participants to record their sleep duration (hours of sleep) for a week using a sleep diary or a wearable sleep tracking device.
  3. At the end of the week, administer a standardized test or assessment relevant to the population (e.g., a cognitive ability test, a job-related test, or an academic exam).
  4. Collect and analyze the data to determine if there is a correlation between sleep duration and test scores.

Analysis:

  • Calculate the correlation coefficient (e.g., Pearson’s r) between sleep duration and test scores across the sample.
  • If there is a statistically significant positive correlation, it suggests that longer sleep duration is associated with better test performance.

This experiment can demonstrate a correlation between sleep duration and test scores, but it cannot establish causation because there could be other confounding variables influencing both sleep and test performance (e.g., stress levels, study habits, or individual differences in cognitive abilities).

Experiment 2: Establishing Causation

To establish a causal relationship between sleep and test performance, we need to manipulate the independent variable (sleep duration) and observe its effect on the dependent variable (test scores) while controlling for other potential confounding variables.

Procedure:

  1. Recruit a sample of participants from the population of interest.
  2. Randomly assign participants to two groups: an experimental group and a control group.
  3. Provide the experimental group with an intervention to manipulate their sleep duration (e.g., providing sleep hygiene education, adjusting their sleep schedules, or using sleep aids under controlled conditions).
  4. The control group maintains their regular sleep patterns.
  5. Administer a standardized test or assessment to both groups after the intervention period.
  6. Collect and analyze the data to compare the test scores between the two groups.

Analysis:

  • Compare the mean test scores of the experimental group (manipulated sleep duration) and the control group using appropriate statistical tests (e.g., t-test or ANOVA).
  • If the experimental group scores significantly higher on the test than the control group, it suggests that the manipulation of sleep duration caused the difference in test performance.

In this experiment, by randomly assigning participants to groups and manipulating the independent variable (sleep duration), we can establish a causal relationship between sleep and test performance, provided that other potential confounding variables are controlled or accounted for.

It’s important to note that these are simplified examples, and in practice, researchers may need to consider additional factors, such as sample size, randomization methods, control variables, and ethical considerations, to ensure the validity and reliability of their experiments.

Elaborate in detail the general principles and practices necessary when one wishes to go beyond mere correlation to show actual causation.

To demonstrate causation rather than mere correlation, researchers must adhere to several crucial principles and practices. Establishing causation means proving that one variable (the cause) produces an effect on another variable (the effect), under controlled conditions. Here’s a detailed look at the general principles and practices necessary for this purpose:

1. Establishing Temporal Precedence

Principle:

  • The cause must be shown to occur before the effect. This temporal order is critical to demonstrate that the changes in the independent variable (the presumed cause) precede changes in the dependent variable (the presumed effect).

Practice:

  • Longitudinal Studies: Conduct studies that track changes over time to confirm that the causal variable precedes the effect in real-world scenarios.
  • Experimental Designs: Use controlled experiments where the researcher manipulates the independent variable before measuring the dependent variable.

2. Controlling for Confounding Variables

Principle:

  • Confounding variables are external variables that might affect the dependent variable, thereby confusing the effect of the independent variable.

Practice:

  • Random Assignment: Use randomization to assign participants to different groups (experimental vs. control) to ensure that the groups are statistically equivalent on all known and unknown confounders.
  • Matching: Pair participants possessing similar characteristics except in the variable of interest, isolating the effect of the independent variable.
  • Statistical Controls: Use regression or other statistical methods to control for potential confounders.

3. Establishing a Causal Mechanism

Principle:

  • A mechanism or a theoretical rationale should explain how the cause affects the effect. This involves understanding and demonstrating the process or pathway through which the effect is produced.

Practice:

  • Mediator and Moderator Analysis: Identify and test mediator variables that carry the effect of the independent variable to the dependent variable, and moderator variables that change the strength or direction of this impact.
  • Path Analysis: Use statistical models like Structural Equation Modeling (SEM) to test the theoretical causal model and the interrelations between multiple variables.

4. Using Strong Research Designs

Principle:

  • The research design should robustly test the causal relationship, minimizing biases and errors.

Practice:

  • Randomized Controlled Trials (RCTs): Considered the gold standard for testing causation, where participants are randomly assigned to either the treatment or control group to measure the effect of an intervention.
  • Quasi-Experimental Designs: Employ when randomization isn’t possible. These designs include methods like interrupted time series or propensity score matching to approximate randomization effects.
  • Repetitions and Replications: Conduct the study under different conditions and settings or replicate using different samples to strengthen the causal inference.

5. Ruling Out Alternative Explanations

Principle:

  • No other plausible alternative explanations should exist for the observed relationship; the causal connection should be the most reasonable explanation based on the evidence.

Practice:

  • Multiple Studies: Use various studies and data sources to rule out alternative explanations.
  • Peer Review and Publication: Subject findings to peer review where other experts in the field can critique and validate the results and methodologies.
  • Sensitivity Analysis: Test how sensitive results are to changes in the model assumptions.

6. Generalization of Findings

Principle:

  • The findings from the causal research should be applicable in settings outside of the study context, enhancing their external validity.

Practice:

  • Cross-Cultural and Cross-Population Studies: Conduct research across different settings and populations to see if the causal relationships hold universally.
  • Scaling Up Studies: Transition from pilot studies to larger, more diverse samples to test the robustness and generalizability of the causal inference.

In essence, moving from correlation to causation involves careful experimental or quasi-experimental design, thorough and rigorous methods for controlling external variables, a logical causal mechanism, and replication across diverse settings. These practices ensure that the findings are not only statistically significant but also practically meaningful, providing a reliable basis for further research, policy-making, and practical application.

To go beyond mere correlation and establish actual causation, researchers need to adhere to several key principles and practices. Here are some general principles and practices necessary when one wishes to show actual causation:

1. Temporal precedence: One of the fundamental requirements for establishing causation is that the potential cause must precede the effect in time. In other words, the proposed cause must occur before the observed effect. This principle helps rule out the possibility of the effect causing the presumed cause.

2. Controlled experiments: Controlled experiments are considered the gold standard for establishing causation. These experiments involve manipulating the independent variable (the potential cause) while holding all other relevant variables constant (control variables) and observing the impact on the dependent variable (the effect). Random assignment of participants to experimental and control groups helps ensure that any observed differences can be attributed to the manipulated independent variable.

3. Ruling out alternative explanations: To establish causation, researchers must rule out alternative explanations or confounding variables that could potentially account for the observed relationship between the variables of interest. This can be achieved through careful experimental design, statistical control techniques (e.g., multiple regression analysis), or by explicitly measuring and controlling for potential confounding variables.

4. Strength of the relationship: A stronger correlation or association between the variables of interest increases the plausibility of a causal relationship. However, it is important to note that even a strong correlation does not necessarily imply causation; other criteria must also be met.

5. Consistency of findings: Consistent findings across multiple studies, using different methodologies, populations, and contexts, increase confidence in a causal relationship. Replication of results is essential for establishing the robustness and generalizability of causal claims.

6. Theoretical plausibility: Causal claims should be supported by theoretical frameworks or well-established scientific principles that provide a plausible explanation for the observed relationship. Theories can guide the formulation of hypotheses and inform the design of studies to test causal mechanisms.

7. Dose-response relationship: Observing a dose-response relationship, where changes in the level or intensity of the potential cause correspond to changes in the magnitude of the effect, can strengthen the case for causation.

8. Elimination of plausible alternative causes: Researchers should strive to eliminate or account for other plausible alternative causes that could explain the observed relationship. This may involve additional experiments, statistical controls, or logical reasoning to rule out alternative explanations.

9. Longitudinal studies: Longitudinal studies, which involve repeated observations of the same individuals over an extended period, can provide valuable insights into the temporal dynamics of causal relationships and help establish the precedence of the cause over the effect.

10. Converging evidence from multiple methods: Combining evidence from different methodological approaches, such as experiments, observational studies, and qualitative investigations, can strengthen the case for causation by triangulating findings from multiple sources.

It is important to note that establishing causation is a complex endeavor, and researchers often rely on a combination of these principles and practices to build a compelling case for causal relationships. The specific strategies and methodologies employed may vary depending on the research question, the field of study, and the practical constraints of the investigation.

Create a 10-item quiz on the entire thread above.

Quiz: Understanding Correlation and Causation

  1. What does the phrase “correlation is not causation” emphasize?
    • A) A correlation between two variables implies they are causally related.
    • B) A correlation between two variables does not imply one causes the other.
    • C) A causal link between two variables implies correlation.
    • D) None of the above.
  2. Which statistical measure is used to describe the relationship between two variables in terms of correlation?
    • A) Mean deviation
    • B) Correlation coefficient
    • C) Standard error
    • D) Variance
  3. What is a confounding variable?
    • A) A variable that the researcher is trying to manipulate.
    • B) A variable that may falsely suggest or hide the effect of other variables.
    • C) The variable being measured for effect.
    • D) The variable used to measure the effect.
  4. What is the gold standard for demonstrating causation in scientific research?
    • A) Observational study
    • B) Correlational study
    • C) Randomized Controlled Trial (RCT)
    • D) Cross-sectional study
  5. Why are randomized controlled trials considered essential for establishing causation?
    • A) They eliminate the need for a control group.
    • B) They use a large number of test subjects.
    • C) They control for confounding variables by randomly assigning subjects to groups.
    • D) They require less statistical analysis.
  6. Which method involves pairing participants who share similar characteristics except in the variable of interest?
    • A) Matching
    • B) Randomization
    • C) Stratification
    • D) Regression
  7. What type of analysis is used to test the theoretical causal model involving multiple variables?
    • A) Correlation analysis
    • B) Regression analysis
    • C) Path analysis
    • D) Variance analysis
  8. What is necessary to establish before claiming causation between two variables?
    • A) The effect must occur before the cause.
    • B) The cause must be shown to occur after the effect.
    • C) The cause must be shown to occur before the effect.
    • D) The effect must be unrelated to the cause.
  9. How can researchers strengthen their causal inference from a study’s results?
    • A) By conducting the study in a single setting.
    • B) By limiting peer review and critiques.
    • C) By repeating the study under different conditions or with different samples.
    • D) By using smaller, more homogeneous samples.
  10. What is the purpose of a sensitivity analysis in causal research?
    • A) To determine the financial cost of the research.
    • B) To test how sensitive results are to changes in the assumptions of the statistical model.
    • C) To increase the sensitivity of the instruments used in data collection.
    • D) To reduce the sensitivity of the results to errors.

Answers

  1. B) A correlation between two variables does not imply one causes the other.
  2. B) Correlation coefficient
  3. B) A variable that may falsely suggest or hide the effect of other variables.
  4. C) Randomized Controlled Trial (RCT)
  5. C) They control for confounding variables by randomly assigning subjects to groups.
  6. A) Matching
  7. C) Path analysis
  8. C) The cause must be shown to occur before the effect.
  9. C) By repeating the study under different conditions or with different samples.
  10. B) To test how sensitive results are to changes in the assumptions of the statistical model.

Here’s a 10-item quiz on the concept of correlation vs. causation and establishing causation:

  1. What is the key principle conveyed by the phrase “correlation does not imply causation”?
  2. In the example of ice cream sales and drowning incidents, what is the likely third variable accounting for the correlation?
  3. What is the primary goal of the first experiment described to demonstrate correlation between sleep duration and test scores?
  4. How would you analyze the data in the first experiment to determine if there is a correlation?
  5. What is the main purpose of randomly assigning participants to groups in the second experiment?
  6. If the experimental group in the second experiment scores significantly higher than the control group, what can be inferred?
  7. What is the fundamental requirement for establishing causation in terms of the temporal relationship between variables?
  8. Why is it important to rule out alternative explanations or confounding variables when establishing causation?
  9. Provide an example of a principle or practice that can strengthen the case for causation.
  10. Why is it beneficial to combine evidence from multiple methodological approaches when investigating causal relationships?

Answers:

  1. The phrase emphasizes that a correlation between two variables does not necessarily mean that one variable is causing the other.
  2. Hot weather, which increases both ice cream consumption and swimming (and thus the risk of drowning).
  3. To observe the correlation between sleep duration and test scores without manipulating any variables.
  4. Calculate the correlation coefficient (e.g., Pearson’s r) between sleep duration and test scores across the sample.
  5. Random assignment helps ensure that any observed differences can be attributed to the manipulated independent variable (sleep duration).
  6. It suggests that the manipulation of sleep duration caused the difference in test performance.
  7. The potential cause must precede the effect in time.
  8. To rule out the possibility that the observed relationship is due to other factors or variables influencing both the potential cause and effect.
  9. Observing a dose-response relationship, where changes in the level or intensity of the potential cause correspond to changes in the magnitude of the effect.
  10. Combining evidence from different methodological approaches (experiments, observational studies, qualitative investigations) helps triangulate findings and strengthen the case for causation.

Provide 15 discussion questions relevant to the content above.

  1. How does the concept of “correlation is not causation” impact the interpretation of statistical data in everyday news articles?
  2. Discuss the implications of ignoring the principle “correlation does not imply causation” in healthcare research.
  3. What are some common pitfalls researchers might encounter when trying to establish causation from correlation? How can these be avoided?
  4. How might confounding variables complicate the analysis in studies seeking to establish causation?
  5. Compare and contrast randomized controlled trials with quasi-experimental designs in terms of their effectiveness at establishing causation.
  6. Why is temporal precedence crucial in establishing causation? Provide an example from real life where this principle applies.
  7. Discuss the role of mediators and moderators in understanding complex causal relationships. Can you think of a scenario where these would be critically important?
  8. Explore the ethical considerations in randomized controlled trials, especially in vulnerable populations. How can these be mitigated?
  9. What are some challenges in using matching as a method to control for confounding variables? Are there situations where it might not be effective?
  10. Discuss how sensitivity analysis can enhance the credibility of causal research findings. What does it reveal about the robustness of the study’s conclusions?
  11. Explain the importance of peer review in the context of publishing findings that claim causation. How does peer review help improve the quality of such research?
  12. What is the importance of establishing a causal mechanism in research? Discuss with an example where knowing the causal mechanism was pivotal.
  13. How does replication across different settings help in strengthening causal inference? Can you think of a research topic where this would be particularly challenging?
  14. Why might researchers choose path analysis to explore the relationships among several variables? Discuss the strengths and limitations of this method.
  15. Consider a case where correlation is mistaken for causation. What societal or personal consequences might arise from this error, particularly in policy-making or medical advice?

These discussion questions are designed to facilitate deep thinking and conversation on the intricate differences between correlation and causation, the methodologies to establish causation, and the broader implications of these concepts.

Here are 15 discussion questions relevant to the content on correlation vs. causation and establishing causation:

  1. Can you provide an example from real life where a correlation between two variables was mistakenly interpreted as a causal relationship?
  2. Why is it important to understand the difference between correlation and causation in scientific research and decision-making?
  3. In the example experiment demonstrating correlation between sleep duration and test scores, what are some potential confounding variables that could influence both sleep and test performance?
  4. How would you design an experiment to investigate the causal relationship between exercise and mental health?
  5. What are some potential challenges or limitations in establishing causation through controlled experiments?
  6. How can longitudinal studies contribute to our understanding of causal relationships?
  7. Can you provide an example of a theoretical framework or scientific principle that could support a causal claim in a specific research area?
  8. Why is it important to replicate findings across multiple studies when investigating causal relationships?
  9. How can researchers account for or control confounding variables in observational studies when establishing causation is not feasible through experiments?
  10. Can you think of a research question where it might be unethical or impractical to conduct a controlled experiment to establish causation?
  11. Discuss the role of statistical techniques, such as multiple regression analysis, in ruling out alternative explanations and strengthening causal claims.
  12. How might qualitative research methods, such as interviews or case studies, contribute to understanding causal mechanisms or processes?
  13. Can you provide an example of a dose-response relationship that supports a causal claim in a particular context?
  14. What are some potential limitations or critiques of the principles and practices discussed for establishing causation?
  15. Discuss the importance of interdisciplinary collaboration and converging evidence from multiple fields or perspectives when investigating complex causal relationships.

Table of Contents: (Click any link below to navigate to that section.)

  1. Elaborate on the notion that “correlation is not causation”.
  2. Take the question of whether a good night’s sleep is correlated with or causes better scores on tests taken the next day. Design 2 experiments. The first designed to show merely a correlation, and the second designed to show causation.
    1. Experiment 1: Demonstrating Correlation
    2. Experiment 2: Demonstrating Causation
    3. Experiment 1: Demonstrating Correlation
    4. Experiment 2: Establishing Causation
  3. Elaborate in detail the general principles and practices necessary when one wishes to go beyond mere correlation to show actual causation.
    1. 1. Establishing Temporal Precedence
    2. 2. Controlling for Confounding Variables
    3. 3. Establishing a Causal Mechanism
    4. 4. Using Strong Research Designs
    5. 5. Ruling Out Alternative Explanations
    6. 6. Generalization of Findings
  4. Create a 10-item quiz on the entire thread above.
    1. Quiz: Understanding Correlation and Causation
    2. Answers
  5. Provide 15 discussion questions relevant to the content above.

Leave a comment

Phil Stilwell

Phil picked up a BA in Philosophy a couple of decades ago. He occasionally teaches philosophy and critical thinking courses in university and industry. He is joined here by ChatGPT 4, GEMINI, CLAUDE, and occasionally Copilot, his far more intelligent AI friends. The five of them discuss and debate a wide variety of philosophical topics I think you’ll enjoy.

Phil curates the content and guides the discussion, primarily through questions. At times there are disagreements, and you may find the banter interesting.

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    This article includes lengthy dialogues involving Aristotle discussing his philosophical ideas with various individuals, such as a curious teenager, another philosopher exploring his philosophy, and a critical philosopher confronting him on weaknesses in his philosophy.… Read more

  • Detecting Bad Science

    This article provides insight into spotting bad science, vulnerable science domains, pressures driving improper science, questions to ask when evaluating scientific claims, a quiz on scientific claims, and discussion questions. It covers sensationalized headlines, conflicts… Read more

  • Our View of Humanity

    A distorted view of human nature can severely distort our beliefs about events involving humans. Each of the distorted views presented impacts our perceptions and interactions. Real-life tragedies, such as those involving Ted Kaczynski and… Read more

  • Appreciating our Insignificance 

    Accepting our cosmic insignificance offers advantages such as humility, perspective, unity, innovation, and resilience. Staying grounded amid ideologies promising cosmic importance involves mindfulness, gratitude, rationality, meaningful connections, and limiting exposure to grandiose narratives. Real-life accounts… Read more

  • Charles Sanders Peirce

    Charles Sanders Peirce was a foundational figure in American philosophy, best known as the progenitor of pragmatism. His ideas significantly influenced logic, the philosophy of science, and semiotics. His lasting mark on philosophy includes pragmatism,… Read more

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