Author: Feng Zhu
Editors: Nayiri Kaissarian, Jimmy Brancho, and Noah Steinfeld
The first part of this post explained what chaos is, how it was first discovered in studies of the solar system, and why chaotic systems can be difficult to understand. In this second part of the post, we will explore what we can do to get a grip on such systems.
Continue reading “Chaos: Not Quite (but Almost) Randomness – Part 2 of 2”
Author: Feng Zhu
Editors: Nayiri Kaissarian, Jimmy Brancho, and Noah Steinfeld
What is Chaos?
Is our solar system stable, or will the orbits of the planets at some point collapse into the Sun? Closer to home: will it rain tomorrow?
Both these questions turn out to be surprisingly tricky to answer for the same underlying reason: the mathematical models we use to understand these systems are chaotic.
Continue reading “Chaos: Not Quite (but Almost) Randomness – Part 1 of 2”
Escrito en inglés por Belle Moyers, traducido al español por Thibaut R. Pardo-García y editado por Attabey Rodríguez-Benítez.
Cuando hablamos sobre problemas científicos, la frase “correlación no implica causalidad” a veces es utilizada. Pero, ¿Qué significa esta frase? La ciencia hace declaraciones sobre causa y efecto. Por ejemplo, el fumar causa cáncer de pulmón, las emisiones de carbón causan cambios climáticos y altas temperaturas causan un aumento en violencia. Claramente, los científicos tienen alguna manera de inferir relaciones causales. Pero, ¿Cómo es que ellos luchan con la idea de que “Correlación no implica causalidad”? Si no utilizan correlación, ¿Qué herramientas utilizan para inferir causalidad?
Continue reading “La ciencia tras bastidores: Correlación y causalidad”Author: Peter Orchard
Editors: Theresa Mau, Belle Moyers, Alisha John
Almost 100 years ago, the English biologist and statistician Dr. Ronald Fisher was enjoying a cup of tea with his Cambridge University colleagues when another biologist, Dr. Muriel Bristol, made an interesting claim. Bristol asserted that just by tasting her tea, she could infer whether the tea was poured into the cup before the milk, or the milk before the tea.
By Belle Moyers
When talking about scientific issues, the phrase “Correlation doesn’t imply causation” is sometimes thrown around. But what does it mean? Science makes statements about cause and effect. Smoking causes lung cancer. Carbon emissions cause climate change. Higher temperatures cause increased violence. Clearly, scientists have some way of inferring causal relationships. But how do they grapple with the idea that “Correlation doesn’t imply causation”? If they don’t use correlation, what tools do they use to infer causation?
Continue reading “Science behind-the-scenes: Correlation and causation”
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