Correlation and Causation in Evolution


Correlation and causation are two important concepts in science, and they are often confused with each other. Correlation refers to the relationship between two variables, while causation refers to the one-way relationship between two variables where one variable causes the other variable to change.

In evolution, correlation and causation are used to explain how organisms change over time. Correlation can be used to identify relationships between different traits of organisms, such as the size of an organism's wings and its ability to fly. However, correlation does not necessarily mean causation. For example, there is a correlation between the size of an organism's brain and its intelligence, but this does not mean that a larger brain causes intelligence.

Causation in evolution is usually explained by natural selection. Natural selection is the process by which organisms with traits that make them better suited to their environment are more likely to survive and reproduce, passing on those traits to their offspring. Over time, this can lead to changes in the population of organisms, and even the formation of new species.

Here are some examples of correlation and causation in evolution:

  • Correlation: There is a correlation between the size of an organism's wings and its ability to fly.

  • Causation: Natural selection has caused organisms with larger wings to be more likely to survive and reproduce, leading to an increase in wing size over time.

  • Correlation: There is a correlation between the size of an organism's brain and its intelligence.

  • Causation: Natural selection may have caused organisms with larger brains to be more likely to survive and reproduce, leading to an increase in brain size over time.

It is important to remember that correlation does not equal causation. Just because two things are correlated does not mean that one causes the other. In science, it is important to be able to distinguish between correlation and causation in order to make accurate explanations about the natural world.

Here are some ways in which neodarwinism can confuse correlation with causation:

  1. Post hoc ergo propter hoc: This is the fallacy of assuming that because one event follows another, the first event must have caused the second event. For example, just because the extinction of the dinosaurs coincided with the rise of mammals does not mean that the dinosaurs' extinction caused the rise of mammals.

  2. Confounding variables: Sometimes, two events may appear to be correlated when there is actually a third, unobserved variable that is causing both of them. For example, smoking and lung cancer are correlated, but this does not mean that smoking causes lung cancer. Instead, both smoking and lung cancer are caused by a third variable, such as exposure to radon gas.

  3. Reverse causation: It is sometimes possible that the apparent causal relationship between two variables is actually reversed. For example, it has been shown that people who are more depressed are more likely to get sick. However, this does not mean that depression causes illness; instead, it is possible that illness causes depression.

  4. Selection bias: This occurs when a sample of data is not representative of the population from which it was drawn. For example, a study of the effects of a new drug that only includes people who are already healthy is likely to overestimate the drug's effectiveness.

  5. Confirmation bias: This is the tendency to favor information that confirms our existing beliefs and to ignore or discount information that contradicts them. For example, someone who believes that neodarwinism is true is likely to be more likely to believe studies that support neodarwinism and to dismiss studies that contradict it.

  6. Oversimplification: Neo Darwinism is a complex theory, and it is often oversimplified in popular culture. This can lead to misunderstandings and confusion about what the theory actually says.

  7. Misinterpretation of data: Even when data is collected and analyzed correctly, it can still be misinterpreted. For example, a correlation between two variables does not necessarily mean that the variables are causally related.

It is important to be aware of these potential pitfalls when trying to draw causal inferences from data. In general, it is best to avoid making causal claims based on correlation alone. Instead, multiple lines of evidence should be considered before concluding that one variable causes another.

Comments

Post a Comment

Popular posts from this blog

Natural Selection has not been accurately measured since Darwin

Image
Francis Crick's term "frozen accident" falsely described the seeming rigidity of the genetic code, its apparent inability to accommodate additional amino acids beyond the standard 20. This observation had puzzled scientists for decades, as the genetic code seems to have reached a plateau in its expansion despite the potential for incorporating new amino acids with novel properties. NeoDarwinian thinking reasoned greater natural selection could occur with more codons. Several hypotheses were put forward to explain this apparent standstill in the genetic code's evolution. Crick and others  eventually applied natural selection to think redundant codons were neutral as to account for neutral selection . The question of why the genetic code stopped incorporating new amino acids remained a fascinating topic in evolutionary biology till Cas9 Crispr. The redundancy of codons, where multiple codons can specify the same amino acid, has long been considered a neutr
Read more

Greatest threat to Evolution in 60 years

Image
60 years of evolution studies are threatened by nonneutral synonymous mutations. Synonymous mutations are mutations that change a codon in a gene but do not change the amino acid that is encoded by that codon. For many years, synonymous mutations were thought to be neutral, meaning that they had no effect on the fitness of an organism. However, recent research has shown that this is not always the case. In fact, many synonymous mutations can have a negative effect on fitness. This is because synonymous mutations can affect the way in which a gene is expressed. For example, a synonymous mutation can change the structure of a gene transcript, which can make it more difficult for the gene to be translated into a protein. Synonymous mutations can also affect the rate at which a gene is transcribed or translated. The discovery that synonymous mutations can be nonneutral has important implications for the study of evolution. For example, it means that we need to be more careful w
Read more

The Origin at 150: Charting a New Evolutionary Voyage on Post-Genomic Seas

Image
“The summary of the state of affairs on the 150th anniversary of the Origin is somewhat shocking: in the post-genomic era, all major tenets of the Modern Synthesis are, if not outright overturned, replaced by a new and incomparably more complex vision of the key aspects of evolution. So, not to mince words, the Modern Synthesis is gone.” -EV Koonin, cited by over 250,000 scientists. The Origin at 150: Charting a New Evolutionary Voyage on Post-Genomic Seas As the 150th anniversary of Charles Darwin's epochal "On the Origin of Species" casts a long shadow, evolutionary biologist Eugene V. Koonin throws down a gauntlet in his essay "The Origin at 150: Is a new evolutionary synthesis in sight?" He contends that the Modern Synthesis, the reigning framework since the 1950s, has crumbled under the weight of post-genomic discoveries. This audacious claim compels us to ask: has the ship of evolutionary understanding truly run aground, and if so, can a new v
Read more