Random Number Generator
Random Number Generator
This version of the generator creates a random integer. It can deal with very large integers up to a few thousand digits.
This version of the generator can create one or many random integers or decimals. It can deal with very large numbers with up to 999 digits of precision.
A random number is a number chosen from a pool of limited or unlimited numbers that has no discernible pattern for prediction. The pool of numbers is almost always independent from each other. However, the pool of numbers may follow a specific distribution. For example, the height of the students in a school tends to follow a normal distribution around the median height. If the height of a student is picked at random, the picked number has a higher chance to be closer to the median height than being classified as very tall or very short. The random number generators above assume that the numbers generated are independent of each other, and will be evenly spread across the whole range of possible values.
A random number generator, like the ones above, is a device that can generate one or many random numbers within a defined scope. Random number generators can be hardware based or pseudo-random number generators. Hardware based random-number generators can involve the use of a dice, a coin for flipping, or many other devices.
A pseudo-random number generator is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. Computer based random number generators are almost always pseudo-random number generators. Yet, the numbers generated by pseudo-random number generators are not truly random. Likewise, our generators above are also pseudo-random number generators. The random numbers generated are sufficient for most applications yet they should not be used for cryptographic purposes. True random numbers are based on physical phenomena such as atmospheric noise, thermal noise, and other quantum phenomena. Methods that generate true random numbers also involve compensating for potential biases caused by the measurement process.
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The random number generator provides a set of random numbers according to user-specified options such as range, repeat, and sort.
48, 9, 49, 11, 17, 22, 16, 37, 45, 41, 4, 36, 43, 10, 28, 27, 47, 25, 21, 33
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Table of Contents
The use of random number generators, the random and a non-random sequence difference, the types of random number generators.
- Google's Generator
The Linear Congruent Method
Vacuum quantum fluctuations, carbon nanotube generator, cubes and the electronic frontier foundation (eff), a quantum random number generator from a nokia smartphone.
A random number generator is a process of getting a random number every time it is needed, without the ability to define a pattern from previously generated numbers. This number can be generated either by an algorithm or a hardware device.
Generating random numbers is needed for various tasks, from computer games to common applications. For example, the system uses a random number generator to display a random banner or a random ad unit on a website. In cryptography, random numbers are also used to make a unique cipher or key.
Random number generation is used to generate numbers or text for captcha, encryption, generating salt for storing passwords, a password generator, an order of cards in an online casino, decision-making, sampling, and simulation.
The random number generator algorithm is often used in video games. Even if you play at the same level in a game, it will not be precisely the same every time you try to complete a mission. Maybe the differences will not be seen in the location or mission. Still, they will be seen in the number of enemies approaching and the areas where they appear, the climate changes, and the various obstacles encountered. This makes the game more exciting.
Let there be a sequence of numbers: 1 , 2 , 3 , 4 , 5 . Is it random?
A random variable is a variable that takes on one of some values as a result of a trial. And you cannot accurately predict the occurrence of a particular value before it appears.
Let's say that the given numbers were obtained by typing on one of the top rows of the keyboard. In this case, it turns out that this combination is not random because, after 5, the following number, 6, can be predicted with high probability.
The sequence will be random only if there is no dependence between symbols.
The basic condition, which is extremely important for the correct and fair principle of the random number generator, is an absolutely equal probability of any possible number that could fall out in this system. This indicates complete independence of the randomness factor and independence of what other numbers fell out before or after the random number.
For example, suppose you roll a six-sided die for the first time. In that case, you can have absolutely any number from 1 to 6 falling out with the same probability. Regardless of your number, you can roll the dice again with the same chance of getting the same number on the second, hundredth, or thousandth roll.
The sequence of digits in the number Pi appears to be non-repetitive, and to many, it might seem random. Suppose our hypothetical generator relies on the bit representation of Pi, beginning from an undisclosed point. Such a generator might be unpredictable in many contexts, potentially passing certain tests for randomness. However, relying on Pi for cryptographic purposes poses risks. If an adversary determines the specific segment of Pi being used, they can predict both preceding and following segments, compromising the security of the system.
The U.S. National Institute of Standards and Technology proposed the "Statistical Test Package for Random and Pseudorandom Number Generators for Cryptographic Applications." It includes 15 statistical tests whose purpose is to determine the measure of randomness of bits generated by either hardware or software generators.
There are two types of random number generators (RNG): true random number generators (TRNG) and pseudo random number generators (PRNG). TRNGs use physical phenomena to generate numbers, while PRNGs use mathematical algorithms.
A true random number generator algorithm is created with a hardware device that uses tiny physical processes to generate random numbers, namely entropy. Entropy is pure, unfiltered chaos.
True random number generators use physical phenomena such as:
- thermal noise,
- electromagnetic noise,
- quantum mechanics, and others.
A true RNG is commonly used in security-oriented systems worldwide and some forms of encryption.
Random number generators use entropy sources to accumulate entropy and obtain the initial value (seed) needed by random number generators.
The pseudorandom number generator algorithm is used in areas with no security concerns. Randomness helps to avoid repetition and make the process more attractive to the end user. Implementing the technology of pseudorandom number generators is cheaper and faster because it does not require hardware and can easily be built into program code. Although the process is not entirely random and is determined based on an algorithm, it is more suitable for games and programs.
The PRNG uses a single initial value, from which its pseudo-randomness follows. At the same time, the true random number generator always generates a random number by having a high-quality random value provided at the beginning by various sources of entropy.
Pseudorandom number generation has its drawbacks. They work because they are random to the untrained eye. However, suppose you knew the initial value for a particular sequence of PRNGs. In that case, you could predict which numbers would be next.
Speed-playing video game enthusiasts often exploit this vulnerability—they call it manipulation of the PRNG. They make the game run predictably so they can pass it as quickly as possible. Fortunately, it does not entail critical problems.
But there are times when predicting random numbers is much more critical. For example, when creating security keys.
If the attacker figures out the initial value used to create RSA keys in TLS certificates, he could potentially decrypt network traffic. This means he can get passwords and other personal information sent over the Internet.
In these situations, a more secure way to get random numbers, i.e., a true random number generator, is needed.
One of the most popular algorithms for pseudorandom number generators is the Linear Congruent Method. It is used in simple cases and has no cryptographic strength. Derrick Henry Lehmer proposed the linear congruent method in 1949.
To implement the generation of numbers by this method, we need to pick four numbers:
m > 0 , modulo
0 ≤ a ≤ m , the multiplier
0 ≤ c ≤ m , the increment
0 ≤ X₀ ≤ m , the initial number
The sequence of random numbers itself is generated using the formula:
Xₙ₊₁ = (aXₙ + c) mod m
It is worth noting that this method depends on the choice of parameters.
For example, for the following set:
X₀ = 3, a = 4, c = 5, m = 6
we get a short repetitive sequence of
3, 5, 1, 3, 5, 1
which does not look random.
But it's worth changing the parameters to something else:
X₀ = 2, a = 85, c = 507, m = 1356
And the scatter of results becomes more unpredictable. You must choose the numbers for this algorithm with special care.
2, 677, 1100, 443, 194, 725, 1112, 107, 110, 365, 344, 1271, 62, 353, 680, 1355, 422, 1121, 872, 47, 434, 785, 788, 1043, 1022, 593, 740, 1031, 2, 677, 1100, 443, 194, 725, 1112, 107, 110, 365, 344, 1271, 62, 353, 680, 1355, 422, 1121, 872, 47, 434, 785, 788, 1043, 1022, 593, 740, 1031, 2, 677, 1100, 443, 194, 725, 1112, 107, 110, 365...
Although the linear congruent method generates a statistically good pseudorandom number sequence, it is not cryptographically robust. Generators based on the linear congruent method are predictable, so you cannot use them in cryptography.
Linear congruent method generators were first cracked by Jim Reeds in 1977 and then by Joan Boyar in 1982. She also managed to break quadratic and cubic generators. Thus, they proved the uselessness of generators based on congruent methods for cryptography. However, generators based on the linear congruent method retain their usefulness for non-cryptographic applications, for example, for simulations. They are efficient and show good statistical performance in most empirical tests.
Modern Hardware Random Number Generators
The randomness effect in the device depends on the quantum physical process of photon emission in semiconductors and the subsequent detection of individual photons. In this process, photons are detected randomly, independently of each other, and the timing information of the detected photons is used to generate bits.
CloudFlare's San Francisco office houses random number generators called "lava lamps". Such a lamp is a glass vessel filled with transparent oil and translucent paraffin. Paraffin is slightly heavier than oil, but when heated slightly, it becomes lighter and floats up.
The movement of the liquids is monitored by several cameras that take snapshots. The snapshots are converted into numbers, from which encryption keys are then generated.
The other two CloudFlare offices use different ways to get random values. In London, a camera captures the movements of three chaotic pendulums. In Singapore is used a Geiger counter that measures the radioactive decay of a small piece of uranium. In the latter case, uranium is used as a "data source" because radioactive radiation is characterized by the randomness of each decay act.
HotBits is a site that provides true random numbers generated by a Geiger counter that registers ionizing radiation to everyone. You fill out a request form on the site specifying the number of random bytes and choose your preferred method of obtaining the data. Once the random numbers are provided to the customer, they are immediately removed from the system.
Contrary to its name ("vacuus"-empty), the vacuum cannot be considered empty. Under the Heisenberg uncertainty principle, virtual particles are born and die unceasingly.
Canadian physicists have designed a fast and structurally simple random number generator based on vacuum fluctuations. The generator consists of a pulsed laser with a high frequency of radiation, a medium with a high refractive index (diamond), and a detector. Passing through the diamond, each pulse on the sensor shows different characteristics depending on the vacuum field fluctuations encountered on the photons' path.
In the spectrum of the scattered radiation, spectral lines appear. Because of the unpredictability of the vacuum fluctuations, these lines differ in unpredictable ways each time.
This method combines compactness with generated thermal noise.
The researchers built a random number generator from a static random access memory cell printed with special inks containing semiconducting carbon nanotubes. The memory cell uses thermal noise fluctuations to generate random bits.
The carbon nanotube generator can be printed on flexible plastic substrates, allowing it to be integrated into tiny, flexible electronics devices, wearable sensors, disposable labels, and smart clothing items.
The company has proposed a simple way to create secure passwords using a physical random number generator. It is based on dice.
For example, you roll five dice at a time and write down the resulting numbers. The dice are arranged from left to right as follows: 63131. Next, you open a long list of words on the EFF website to find the corresponding word next to 63131. That word is "turbofan."
You can repeat this procedure several times. For example, five times. You may end up with a phrase of five words. Let's say, "turbofan purge unfitting try pruning." If you know how to use the rules of mnemonics, you can memorize phrases like these.
In 2014, the University of Geneva created a QRNG device that used the camera of the Nokia N9 smartphone.
The smartphone camera counted the number of photons hitting each pixel. The light source was a standard LED. Each pixel in the 8 MP camera detected about 400 photons in a short time. The total number of photons at all pixels was then converted into a sequence of random numbers.
Random Number Generator
Use this tool to generate one or more numbers randomly from a defined range of numbers. You can choose the number of random numbers you want to generate and set a custom min-max range.
Type in how many random numbers you want to generate. The minimum amount is 1 and the maximum is 10,000.
Min and Max
Set a minimum number and a maximum number. The randomly generated numbers will be within this custom range. Make sure Min is lower than Max , or else you will encounter an error.
Enable the "Sort" option if you want the randomly generated numbers to be sorted according to the natural order, also known as the human way of sorting numbers (1, 2, 3, 4, 5, 6, 7, 8, 9, 10).
Select the number separator of your choice: line break, comma, space, or comma plus space. By default, the generated numbers are separated by a new line.
When enabled, the "No repeats" option prevents the same number from being generated more than once. Make sure that the amount of numbers you're generating ("How many") is less than or equal to the difference between the min and max range, or else you'll encounter an error. In this case, you either want to increase the max range, lower the amount, or turn off the "no repeats" option.
Generate Random Strings and Numbers
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Random number generator
1 unique number from 0 to 100 generated at 2024-02-14 16:07:39
Download your random number
Download 1 random numbers from 0 to 100 generated at 2024-02-14 16:07:39 in Excel (.xlsx), CSV (.csv), JSON (.json) or Text file (.txt) format
Random Data Generator API
Random integer numbers, number sequence, phone numbers, credit card numbers, social numbers, api generator for your project.
Randommer wants to be a tool for all kind of people: from persons who want to validate mobiles phone to developers who wants to build modern applications. Use our online services to get data for your needs or check directly our website to resolve your business requirements. We offer and we build every day new functions to solve the daily problems of the people.
You can use our API to build your project without developing from scratch the base functions to generate data like numbers, telephones, and text. Randommer.io offers several utility services and we use the newest technologies like RESTful services and fast hosts to be a simple and modern tool. You can call our services to generate random information using REST. Using HTTPS we secure our connection between us.
- Generate Random Integer
- Random Sequence Generator
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Frequently asked questions
- What Randommer can offer me? Randommer can offer a collection of various tools that can generate online dummy data and JSON REST API to generate and validate data.
- Is randommer.io a real data generator? Our data follow all rules regarding validation. For example, a phone number that is generated by us is valid but is not real so you can use it however you want.
- Is Randommer free of charge? Yes, Randommer is free and will remain free. You have a daily quota of 1000 API calls every day. So if you don't spam us is fine.
- Do you offer an API service? Yes, almost all Randommer services have API support because we know that you need data directly in your project.
- Can I get the data in JSON format? Yes. If you use our API you can get data in JSON.
- Can you give me an example where Randommer can help me? Maybe you build a website and you need some text placeholder or you just want a secure password generator. You can transform text and convert, generate his hashes or even his Caesar encryption.
- Do you plan new features? Yes, we have planned over one hundred useful tools to be available. Randommer wants to be a tool library - the Swiss army knife
- Do you offer support for new tools? Yes, if you have an idea for a new tool, you can contact me at this address
- How can I show my appreciation? You can write an article about this service or just give this link to anyone who needs some mockups.
- Does the API support CORS (Cross-Origin Resource Sharing) Yes, you can call our services from any domain or application, even from a simple HTML page.
- How Randommer is different from other services? Most of the alternatives don't offer API support or it offers only a low daily quota for free. Most of them provide only one or two services.
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Random Number Generator
Feed the randomizer any number of items (numbers, letters, words, IDs, names, emails, etc.) and it will return them in a truly random order, resulting in a randomly shuffled list. Free online random list generator & list shuffler.
- Using the randomizer
- How many items can the randomizer process?
- Example applications of the randomizer
Random Team Picker
Random awards picker, randomly distributing chores or tasks, shuffling song lists.
- Is the randomized list truly random?
- Shuffling algorithm used in the randomizer
- Randomizer vs. Randomiser
Using the randomizer
Using this list randomizer you can shuffle any list in random order. It uses strong cryptographic algorithms to generate random numbers which are then used in an algorithm for unbiased randomization of the list items (more on this below). The result is a truly randomly shuffled list consisting of the initial items.
To use the tool, simply enter a list of items of any sort, one item per row (copy/pasting from a spreadsheet works great). It could be a list of numbers, words, names, emails, countries, songs, tasks, and so on. This website uses a secure connection over HTTPS and does not store any of the information you enter in the field above, so the randomizer should generally be safe to use even for somewhat sensitive information, but it is best that you consult your information security officer if you have any concerns.
The easiest way to retrieve the randomized list is to select it all (Ctrl+A on a PC), and then copy it (Ctrl+C on a PC).
How many items can the randomizer process?
The maximum number of items per list the randomizer will process is 100,000 . If your items contain a lot of text this number may be subject to further restrictions such as the maximum request size allowed, or the memory limit allotted to our scripts. If you run into such issues, consider replacing the items with short numerical item IDs before feeding them to the shuffler as a list.
Example applications of the randomizer
A free online randomizer like this can have many possible uses. Here we list a few more common ones.
The classic way to randomly distribute players across teams in a sports game or board game is to randomly draw names out of a hat. Using the list randomizer you can spread players into two or more teams fairly and without bias. Simply enter all the player names and click "Randomize list". If you need two teams, select the first half of the shuffled names for team 1 and the second for team 2. A similar process can be followed for any number of teams as long as the total number of players is divisible by the number of teams to fill. The same logic can be used to distribute students for school group projects.
Despite the above examples, it is more convenient to use our dedicated random team generator which supports multiple teams easily.
If you have a number of names, emails, or identifiers of some sort, and you want to randomly sort them so that only the top 1, 5, 10 etc. receive an award, you can enter the list and randomize it to obtain the list of winners. If the awards are numbered from, say, 1 to 10, you can dole out the awards following the order of the shuffled list.
In case you need to distribute chores or tasks over a group of people or over several days simply list the chores or tasks and shuffle them with our software. Then start with the first on the list and proceed till the end. Similarly, you can randomize a list of your child's names to determine in what order they will do the dishes, sweep the floors, or throw out the garbage in the next few days.
If you are a schoolteacher, you may use this to randomly pick students for different home assignments, projects, etc. While a physical spinning wheel might be more fun, using an online list randomizer is easier.
In yet another scenario, you might want to shuffle a list of songs, books, games, or other things you want to get in random order. In this sense our tool can be used as a random order generator.
These are just several scenarios for using a list shuffler, but we are sure you can come up with many more.
Is the randomized list truly random?
If your requirements for the randomness of the shuffle are high, you may be wondering if you can trust that our randomizer engine results in unbiased shuffles . Bias here has the technical meaning of 'systematically skewed'. In list shuffling a systematic skewness will be exhibited if items in a certain position in the initial list have an expected probability for ending up in a given position in the shuffled list which is different than the probability of ending up in any other position.
In order to check the randomizer unbiasedness , we devised a straightforward simulation , consisting of shuffling a list of 4 items 4,000,000 times. For simplicity, the four items were the numbers 1, 2, 3, and 4, fed to the randomizer each time in that order.
The results were collected and for each of the four possible positions we summed up the numbers that ended up there in the 4,000,000 simulations. This is the resulting histogram:
As you can see, there is no bias towards any of the positions, each having a sum of approximately 1,000,000 out of the total sum of 4 million. A statistical goodness-of-fit test was conducted which resulted in a p-value of 0.86, firmly indicating conformity to the expected uniform distribution. As a further precaution we examined the distributions of ones, twos, threes and fours in all positions and found them to be uniformly distributed across them. Goodness-of-fit tests were performed for each of these and the results were again within the expected bounds, confirming that our randomizer produces truly random shuffled lists which should be safe to use in any application requiring robust randomness in the shuffle.
Shuffling algorithm used in the randomizer
For this random list generator we employ the robust, efficient, and unbiased Fisher–Yates shuffle  , also known as the Knuth shuffle . In particular, we implement its modern variant (the initial algorithm was for pen, paper, and a dice!) as described in Richard Durstenfeld's 1964 work  . The algorithm was popularized by D.Knuth in his book "The Art of Computer Programming".
The random numbers required for the algorithm's application are generated using a cryptographic pseudo-random number generator (CPRNG) supplied by urandom, the Linux kernel's random number source.
Randomizer vs. Randomiser
A brief note for those of you who might be confused and wondering as to the correct spelling of the word. Both are correct, however. 'Randomizer' is the American version while 'Randomiser' is the preferred spelling in British English.
1 Fisher, R.A., Yates, F. (1948)  "Statistical tables for biological, agricultural and medical research" (3rd ed.), London: Oliver & Boyd pp.26–27.
2 Durstenfeld, R. (1964) "Algorithm 235: Random permutation", Communications of the ACM 7(7),p.420. DOI:10.1145/364520.364540
3 Knuth, D. E. (1969). "Seminumerical algorithms. The Art of Computer Programming." 2, Reading, MA: Addison–Wesley pp. 139–140.
Cite this randomizer & page
If you'd like to cite this online randomizer resource and information as provided on the page, you can use the following citation: Georgiev G.Z., "List Randomizer" , [online] Available at: https://www.gigacalculator.com/randomizers/randomizer.php URL [Accessed Date: 14 Feb, 2024].
Features of this random number generator:
- Generate sequence using a loop
- Speed loop that lets you control the speed of random generation
- History of generated numbers for both the sequence and the loop
- Copy numbers to clipboard
- Delete or Copy History
- Create favorite random number generators
- Remembers recently used random number generators
- Remembers last generated number
- Use Seed for a Seeded RNG to generate the same sequence again
- Reset Seed per session or remember the seed for longer
- Option to allow numbers to repeat in the loop or the sequence
- Notifies when all numbers in the sequence are generated
- Auto-stop loop when it has looped through the range of numbers
- Exclude specific numbers from the list
- Select odd, even or half for games
- Show total sum of the random numbers
- Specific shortcuts for single digit ranges, pin codes, lottery, dice and coins
- Lucky touch screen that allows you to select lucky numbers using your touch
- Multiple screens for easy access of presets
Random Number Generator App in Playstore
Random values: numbers and texts
Random values can be specified for both numbers and text.
Integer values can be defined using either one or two parameters.
The specified parameter defines the length of the random number. Maximum number of digits: 18
If two parameters are specified, they define the upper and lower limit of the random number. The lower limit must be less than the upper limit, and negative values are possible. The generated integer is a value that lies between the upper and lower limit and includes boundaries.
There are two different ways to specify random numbers containing decimal places.
One parameter specifies the length of the random number, the other one specifies the number of decimal places.
The first parameter defines the number of decimal places. The second and the third parameter specify the lower and the upper limit respectively.
Random character strings are created by using parameters that specify the length of the string. Numbers and letters are generated. The maximum length of the string is limited to 1024 .
Random, unique strings containing current timestamps
Random unique strings can be used for value input. They consist of 16 random numbers and characters, based on the current timestamp.
Random strings containing regular expressions
Random character strings can be generated which are limited by regular expressions. The regular expressions must be specified within double quotation marks.
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Tricentis Tosca Manual 14.0 © Tricentis GmbH