Probability is the probability mass distributed over your data with fixed parameters, and likelihood is mass distributed over your model parameters with fixed d

Probability is the probability mass distributed over your data with fixed parameters, and likelihood is mass distributed over your model parameters with fixed data. The absolute most important thing to know about likelihood is that it is not a measure of probability, even thought it looks a lot like probability. If I look at coin flip data, I know the data comes from a coin flip, but any specific count of heads vs tails becomes less and less likely the more flips we do. So likelihood being small tells us nothing on its own. The value of likelihood comes from the framework you use in. If I wanted to make a best guess at what the balance of the coin is then I could find the maximum of the likelihood over all coin balances to get the most representative version of my model. Similarly, I can compare two specific coin biases and determine which is more likely, but that alone can’t tell me anything about the probability of the coin being biased.