Some organisations, like news-sites are dependent on bias. They represent the data in a favourable way. This includes science news. That makes their product more popular.
Then we have organisations that like to represent data overly optimistic or overly dramatic or overly clinical. Again this is to make the product seem better.
Also the peer-review system is a bias creating system. The "best" articles confirm the bias of the peers.
Additionally science-communities are also very strong in excluding unwanted ideas. I found that out when I switched departments. So even the article that you wrote, probably has many biases in them.
* Curing bias:
My strategy to avoid bias is to allow many possible hypotheses, which all should be logical in their own "world".
This requires training, and is not something that we see in education or in news. Representing the data as "facts", does not uncover the assumptions and bias that the data was based on. I think that it is also very important to see logical fallacies in the information. In science this there is often an "expert-bias", where one relies on experts that certainly had a bias too.
To go even further, it is very interesting to look at historical biases and facts that changed during the history. This allows us to look at our personal beliefs from a distance, but also more accepting of possible failure. Even very smart persons made big mistakes or were into things that you would not like.
Time shows that things that we first thought were facts, became false later. So it is also important to keep some humour.
https://i.imgur.com/KpF234Y.jpg
Any organism/machine that its main operation is pattern matching and its primary constraint is to lessen change as much as possible (i.e. to minimize energy expenditure as much as possible) is about to be biased at one point.
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[ 3.1 ms ] story [ 22.9 ms ] threadSome organisations, like news-sites are dependent on bias. They represent the data in a favourable way. This includes science news. That makes their product more popular.
Then we have organisations that like to represent data overly optimistic or overly dramatic or overly clinical. Again this is to make the product seem better.
Also the peer-review system is a bias creating system. The "best" articles confirm the bias of the peers.
Additionally science-communities are also very strong in excluding unwanted ideas. I found that out when I switched departments. So even the article that you wrote, probably has many biases in them.
* Curing bias:
My strategy to avoid bias is to allow many possible hypotheses, which all should be logical in their own "world".
This requires training, and is not something that we see in education or in news. Representing the data as "facts", does not uncover the assumptions and bias that the data was based on. I think that it is also very important to see logical fallacies in the information. In science this there is often an "expert-bias", where one relies on experts that certainly had a bias too.
To go even further, it is very interesting to look at historical biases and facts that changed during the history. This allows us to look at our personal beliefs from a distance, but also more accepting of possible failure. Even very smart persons made big mistakes or were into things that you would not like.
Time shows that things that we first thought were facts, became false later. So it is also important to keep some humour. https://i.imgur.com/KpF234Y.jpg