The probability of two people's birthday being the same is actually more likely than many would think. The key thing is to note that it doesn't matter what the first person's birthday is. All we need to work out is the probability that the second person has a birthday on any specific day. This probability is 1/365.25
The probability that they were born on June 10th is 1/365.25. The probability that they were born on February 2nd is 1/365.25 and the probability that they were born on the same day as you is 1/365.25
The probability, over presidents of all organisations, through all of time, is 1.
Slightly more than 1 in 2.
1:30
A birthday attack is a method of code decryption which exploits the birthday paradox - that which explains that within a class of 30 students, there is an assumed probability of two sharing the same birthday of 70 percent.
In probability theory, the birthday problem, or birthday paradox[1] pertains to the probability that in a set of randomly chosen people some pair of them will have the same birthday. In a group of 10 randomly chosen people, there is an 11.7% chance. In a group of at least 23 randomly chosen people, there is more than 50% probability that some pair of them will both have been born on the same day. For 57 or more people, the probability is more than 99%, and it reaches 100% when the number of people reaches 367 (there are a maximum of 366 possible birthdays). The mathematics behind this problem leads to a well-known cryptographic attack called the birthday attack. See Wikipedia for more: http://en.wikipedia.org/wiki/Birthday_paradox
Leaving aside leap years, the probability is 0.0137
His birthday is the same as mine! 12th October
The probability with 30 people is 0.7063 approx.
The probability, over presidents of all organisations, through all of time, is 1.
His birthday is on the 30 of January same as mine berbatov is the best
In a probability sample, each unit has the same probability of being included in the sample. Equivalently, given a sample size, each sample of that size from the population has the same probability of being selected. This is not true for non-probability sampling.
The key feature is that each sample of the given size has the same probability of being selected as the sample. Equivalently, each unit in the population has the same probability of being included in the sample.
1/365 = 0.00274
Nov 20th! The same as mine!
February 27! same as mine :D
I dont have the Same birthday as Justin but mine is March 8th.
Every member in the population has the same probability of being in the sample.Or, equivalently, every set of the given sample size has the same probability of being selected.