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Mis Interpretation Of P Values

  • A low P-value means the observed result is unlikely to have occurred by chance, but it does not prove causation.
  • A high P-value means the observed result could have occurred by chance, but it does not prove there is no relationship.
  • Interpret P-values alongside other evidence and consider alternative explanations; do not use them as the sole basis for conclusions.

P-values are a common statistical measure used to determine the likelihood of a given result occurring by chance. Mis-interpretation of P-values occurs when people draw incorrect conclusions from those values—for example, treating a low P-value as proof of a strong relationship or causation, or treating a high P-value as proof that no relationship exists.

A low P-value indicates that the observed relationship is not likely to have occurred by chance, but it does not establish that one variable causes another. Conversely, a high P-value indicates that the observed relationship could have occurred by chance, but it does not establish that there is no causal or contributing relationship. Mis-interpreting P-values in either direction can lead to incorrect conclusions because other factors, alternative explanations, or additional evidence may change the interpretation.

Imagine a study that examines the relationship between exercise and weight loss. The study finds that people who exercise regularly have a lower P-value for weight loss than those who do not exercise regularly. Some may interpret this to mean that exercise causes weight loss. However, a low P-value simply indicates that the relationship between exercise and weight loss is not likely to have occurred by chance. It does not prove that exercise causes weight loss.

Imagine a study that examines the relationship between pollution and asthma rates. The study finds that there is a high P-value for the relationship between pollution and asthma rates. Some may interpret this to mean that pollution does not cause asthma. However, a high P-value simply indicates that the relationship between pollution and asthma rates could have occurred by chance. It does not prove that pollution does not cause asthma.

  • Mis-interpreting P-values can produce incorrect conclusions, such as assuming causation where none is proven or dismissing a contributing factor because of a non-significant P-value.
  • To avoid mis-interpretation: carefully interpret study results, examine all available evidence, consider all possible explanations, and recognize that P-values are only one piece of the evidence and should not be the sole basis for conclusions.
  • P-value
  • Causation
  • Chance
  • Relationship between variables