How is a 50% confidence level analogous to flipping a coin? I'm rather new to statistics. I'm learning on my own and I'm trying to understand confidence levels. I read, "How Confident Do You Need to be in Your Research?" by Jeff Sauro, Ph.D., 5 Jan 2015, and in it he states:

if you find that there’s little or no downside to being wrong, and if
you have to pick among poor alternatives, 51% confidence is at least
better than flipping a coin.

This seems to imply that 50% confidence is equal to flipping a coin, but I disagree. My logic is that flipping a fair coin has 0% confidence it will land on heads, this means there's an equal chance it could land on heads, you have 0 conjecture WHICH it will be. A situation with 50% confidence would be like a "loaded"(??) coin that WILL land on heads 50% of the time. The other 50% of the time the coin has an equal chance of landing on heads or tails. I'm clearly incorrect in this thinking and must be confused about what confidence level measures, but am unable to grasp it.
How is a 50% confidence level like flipping a coin? Would it have something to do with confidence interval width?

The context is this phrase

I’m often asked what the best level of confidence to use is. The answer is that it depends on the consequences of being wrong.

For any application where an estimation with a confidence interval is involved you need to determine a convenient/practical desired level of confidence to compute intervals.
To determine this level you need to consider the advantages and disadvantages of making a wrongly estimated interval that does not contain the true parameter.
If you use a 50% confidence interval then this is like flipping a coin. Half the time you will be wrong and half the time you will be right (or depending on the definition of the confidence interval, at least half the time you are right). If you have a business where the cost of a being wrong is equal to the benefit of being right, then a 51% confidence interval can make you money in the long run in a similar way as the small house advantage of a casino.
It is an extreme example to show how the desired confidence level can vary based on the application. The proper level depends on the costs and benefits.

It is a bit black-and-white thinking. The costs and benefits are often not so discrete. Possibly in the case of betting it is. E.g. if you are betting on horse races then you either lose or win. But in some business situation you can have a varying degree of costs and benefits depending on how far away from the actual true value you are. Say you estimate that some value is between 2 and 6 with 90% confidence and you act according to this, then the consequences might be more or less dramatic depending on whether the true value is 6.1 or 10.
The confidence intervals are often a rule of thumb and a rough indication. The coin flipping example is an example how the levels can differ depending on the application, but you are not often gonna find those example applications in real-life.
