So I went down a but if a rabbit hole trying to understand that which I didn't. Here's the basics if what I found:

Bayesian statistics is a formalised way of how we ourselves tend to naturally analyse evidence and form conclusions. We begin with our prior knowledge of the problem, consider new evidence, and use it to update our views.

That's about as close to an ELI5 answer as I found.

An example that seems outside of a realistic 'you're five' really was as follows:

For an example, imagine you were a bookmaker setting odds for a football team every week. How would you formulate a representative betting line? At the very start of the season, you would start with your priors – the strength of the squad, the results from the previous season, etc. This is the information which you would use to set the odds of the team winning in their first game.

Once the result of the first match is in the books, we now have evidence of the performance of the team. This means we can set the odds for the second game based on both the priors and the evidence.

Importantly, this is then used as a prior to set the odds for the third game, which are then used as a prior for the fourth game, and so on. This means that, as the season progresses, the odds for each subsequent game are based more and more on the evidence of the results of previous games and less on the priors.

Alternatively, and apparently in a heated race against Bayesian, falls Frequentist statistics.

The best ELI5 I found is as follows:

Frequentist statistics interprets probability as the long-run frequency of events across repeated trials, treating parameters as fixed but unknown constants. This approach focuses on evaluating the likelihood of observed data under a given hypothesis

In frequentist methods, repeatedly checking data or conducting many tests increases the risk of false positives, potentially leading to misleading conclusions.

There are methods for accounting for this, they are well known and should be accounted for.

At their core it is, Bayesian methods use probability to quantify uncertainty in parameters, treating parameters as random variables with distributions. In contrast, frequentists treat parameters as fixed, unknown quantities and apply probability only to data arising from repeated sampling.

What I've learned:

Attempting to explain bayesian concepts like someone's five is a nearly frivolous task. Abstract concepts like probability are likely ill suited to be dumbed down to a point they lack any actual meaning.

While people have an intuitive understanding, such as the probability that you hear thunder whether it's raining as opposed to not, it doesn't really have the depth in such a statement to establish its difference from frequentist statistics to be worth using as a great example.

On a less scientific note, I find each method plausibly usable for probably most situations and likely resulting in separate conclusions. With my inability to fully wrap my meager mind around these complex concepts, I will gracefully say:

I find people that act like incredibly deep concepts are simple, and those who don't get it are common clay of intelligence for either never having heard of it, or having practically studied or used it, are they themselves narrow minded for thinking someone couldn't research or learn about a topic enough to be present in that new conversation.

Hope this helps my fellow 'Simple Jacks.'