It can be very challenging to assess what information to believe. One simple way to evaluate evidence is via something called "Hill's Criteria", considerations to help assess whether an observation has a *causal* component

I'll describe them using xkcd comics

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Sir Austin Bradford Hill, a statistician & epidemiologist, created a list of guidelines for evaluating whether there is evidence of a causal relationship. He determined the following aspects ought to be considered when assessing causality

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1898525/pdf/procrsmed00196-0010.pdf

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Strength

How big is the effect you are seeing?
Note: Hill suggests that huge effects can suggest causality, however this does not mean small effects cannot

https://xkcd.com/539/ 

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Consistency

This is essentially reproducibility & replicability
Can your analysis be reproduced?
Has anyone been able to replicate your findings?

http://xkcd.com/242/ 
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Specificity

Can the association be pinpointed to a specific cause with no other plausible explanation?

http://xkcd.com/1217/ 

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Temporality

Does the timeline make sense?
In general, the exposure ought to come before the outcome it is said to cause.

http://xkcd.com/925/ 
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Biological gradient

The wording of this point makes it a bit difficult to untangle from the medical application, but generally this refers to a dose effect
Does increasing an exposure yield a change in the outcome.

http://xkcd.com/323/ 
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Plausibility

Does the causal relationship make sense?
This is also a tricky one since plausibility depends on knowledge at the time. If we found it perfectly plausible, we may not need statistics to show the relationship.

http://xkcd.com/605/ 
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Coherence

Similar to plausibility, is there a logical argument that can be made by/to experts in the field regarding causality.
Does it fit into the understanding of the field

http://xkcd.com/1170/ 
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Experiment

If a controlled experiment can take place, this can strengthen the argument for causality
I view this as a general attempt to implement a counterfactual analysis.

http://xkcd.com/1462/ 
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Analogy

Have we seen a similar effect from a similar exposure?

http://xkcd.com/882/ 
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There is lots of incoming information and data these days, hopefully these principles can help a bit with sifting through the noise! I have a blogpost version of this here if that is your cup of tea:

https://livefreeordichotomize.com/2016/12/15/hill-for-the-data-scientist-an-xkcd-story/

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