Just recently understood p-value... I’ve seen many papers mention this value, but I didn’t really know what it meant.
The p-value is the probability of getting the observed experimental results assuming that our hypothesis is not true or has no effect.
For example, if we say that watering plant X every morning makes it grow 20% faster compared to watering it at other times of the day:
If the p-value is high, it means there's a high chance that plant X could grow 20% without us watering it in the morning (suggesting that our hypothesis might not be true).
For instance, if the p-value is 0.1 or 10%, it means there’s a 10% chance that the plant will grow 20% without being watered in the morning.
On Brilliant.org, it explains that the issue with p-values is that they represent confidence in a single experiment only. In practice, a good experiment should be independently replicated and consistently yield significant results. (In Bayesian terms, this means that confidence in the experimental results multiplies with each successful replication.)
The bottom line is... trusting a single paper isn’t very reliable.
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