I'm trying to interprete the Example 1 from the wikipedia page: the likelihood function of a coin flip with a single parameter p expressing how likely a head will come up.
The likelihood is defined as "the plausibility of a parameter value of the statistical model assumed to describe the observed data, given specific observed data".
It's clear to me that the plausibility of a coin being fair (p = 0.5) flipping the coin twice and observing two heads (likelihood = 0.25) is lower than the plausibility of any other model that assumes a higher probability for the head to come up.
But how does one interprete a likelihood = 1 with p = 1, i.e. a model that assumes a head will ALWAYS come up? I'm confident it doesn't mean we are 100% sure that that's the correct model but I cannot really state why not.