Another way to look at it is with a dice roll analogy. Every time you roll a dice, the odds to get a certain number is 1/6. The next roll the odds is the same, regardless of how many times you rolled the dice before. That is what the first graph is supposed to illustrate.
To get successive throw's of a certain number, that is a different question, and indeed is why you calculate the cumulative probability density function, or graph. And that is indeed the 4th graph.
Any suggestions on how I can make it clearer or more intuitive, or indeed if I have made a mistake?
Yeah I think I understood the intention. In that case it's a plot of conditional probability rather than probability density.
I wouldn't change the article, it's clear enough and it's not like they're accurately plotted graphs, just sketches to get your idea across.
Edit: By the way there is a distribution that describes something which is similar to a poisson distribution but with a changing rate, it is typically used for failure rate analysis (time before failure modelling) but could also be used here to describe time before bug discovery: http://en.wikipedia.org/wiki/Weibull_distribution
He's also mixing up discrete and continuous probability distributions. He got pretty much the right answer, but as a statistician it kind of hurts to look at.
Another way to look at it is with a dice roll analogy. Every time you roll a dice, the odds to get a certain number is 1/6. The next roll the odds is the same, regardless of how many times you rolled the dice before. That is what the first graph is supposed to illustrate.
To get successive throw's of a certain number, that is a different question, and indeed is why you calculate the cumulative probability density function, or graph. And that is indeed the 4th graph.
Any suggestions on how I can make it clearer or more intuitive, or indeed if I have made a mistake?