A random number is one chosen by chance from a set of numbers. All the numbers in the specified distribution have an equal probability of being chosen randomly. The most simple random number generator is good old dice. There is a one in six chance of landing a particular number. No matter how many times the die is cast, you have a one-in-six chance of throwing any one of the six numbers. Compare that to the chances you have when drawing prizes from a raffle, for example, and you see a very different type of probability.

If one thousand tickets were sold, the chance of winning the first prize (assuming you bought one ticket) is one in a thousand. The winning ticket does not go back into the bucket of raffle tickets, so the chances of winning the next prize are one in nine hundred and ninety-nine. In addition, raffle tickets might not be sufficiently mixed up to ensure fairness, and a batch of numbers could all be close to each other in the draw.

With a random number generator, the values are uniformly distributed over a predetermined value range (remember the six faces of the dice). The previous throw does not have any influence on the next throw, and what you throw now does not affect one in the future. This simplest of examples illustrates that random distribution is completely fair and unalterable. However, everyone has heard of loaded dice synonymous with cheating, which can make people feel wary of any device that issues up random numbers.

Random numbers are used in digital cryptography, cryptocurrency wallets, computer simulations, statistical analysis, and probability theory. They are used to forecast significant events and drive a whole host of algorithms. Many people are wary about the fairness of random generators and like to believe there is an element of luck or chance when it is all just maths and probability.

Random number generators are the backbone of the online casino industry. With very few exceptions, casino and slot games are purely based on chance, and winning or losing is determined by the numbers the random number generation program throws up.

In the old days of physical, mechanical gaming machines, when someone decided to play the slots, they might have noticed people watching the machines and only playing after a certain number of people had been playing. It was almost as if they knew when the machines would pay out. Truth be told, they did. These machines were set up so that the win could be predicted, and the arcade owners might know just when to put the penny in the slot.

However, that is not the case with online casino games, where every spin has an even chance of winning. Sites often put these up for free, as a way to show you how they work. You can give it a whirl, safe in the knowledge that it does not matter how many times the game has been played before; the odds of winning (or losing) remain constant. It is worth remembering that while the software is trustworthy, gambling is big business, and the online casino needs to make its cut to stay in business. Therefore, no matter how hard you try to outrun luck, the ‘house’ will always win more than you. The fun is in the playing and sometimes feeling you get the upper hand.

Random number generators are also used in multi-factor authentication processes. Anyone who works remotely and has to access sensitive data or bespoke systems will be familiar with apps like Authenticator. Many universities also use the same system for students to work online, submit assignments, and access remote lectures. A user has to log in with email and password and is then asked to enter a six-digit code that has been randomly generated to gain access. The authenticating numbers change every thirty seconds as new ones are generated, so it can be a little nerve-racking when initially logging on.

In the constant battle between cybercriminals and online security experts, random number generators are an essential part of the armory. Making sure you manage your passwords properly and don’t open dodgy email links is only half the battle. If we do not trust the software behind the randomly generated numbers, we will probably struggle to exist in our increasingly digitized world. Short of checking every single number ever issued, it would be impossible to verify that the system is genuinely creating random numbers indisputably. However, if they are doing their job and protecting our details and systems, we have very little option but to trust them.

Digging deeper into random number generation throws up all kinds of complexities like pure-random number generators and pseudo-random number generators. Whatever system is used, billions of numbers can be created in incredibly short periods, and the whole process is somewhat mind-boggling.

If you have seen The Imitation Game about Alan Turing cracking the Enigma Code, you will understand that hoards of humans cannot work fast enough to process the vast quantities of data we now rely on. As the grandfather of computing, Turing’s machine was the original random number generator, and many scientists found it maddening because it was so random and created too much uncertainty. We have been trusting random number generators for over eighty years, often with no idea that they are behind many everyday functions we carry out. They are certainly not a new-fangled thing.