Probability
Probability theory is the study of uncertainty, a relatively new branch of mathematics.
The probability of an event is a number between 0 and 1 representing the likelihood of the event's occurrence. It may be expressed as a simple fraction, decimal fraction, or percentage. When there are a finite number of equally likely outcomes, the probability of an event is given by
.
For example, the probability of a tossed fair coin coming up heads is ½ = 0.5 = 50% because there is one favorable outcome (heads) out of 2 possible outcomes (heads and tails). Another common example is standard deck of 52 playing cards: there are 13 cards in each suit: spades, hearts, clubs and diamonds. After the deck is shuffled thoroughly, the probability that the top card on the deck is a heart is 
, or 25%.
Contents
Formal definition of probability
Let 
be the sample space and 
be a σ-algebra of events. A probability function 
is a function satisfying the Kolmogorov axioms of probability:
for all events 
- If
is a countably infinite pairwise disjoint family of events in , then
Empirical vs Theoretical Probability
Theoretical (or estimated) probability is a calculated probability based upon a set of mathematical or scientific assumptions (including other probabilities). A simple example is the theoretical probability that the chance of rolling two sixes on a pair of unbiased six-sided dice is 1/36 (2.78%).
Empirical (or experimental) probability is calculated simply as the ratio of observed favourable outcomes to total samples taken. Thus a pair of dice might be rolled a thousand times, with an observed occurrence of 26 pairs of sixes. This gives an empirical probability of 26/1000 or 2.60%. With a larger number of samples, the empirical probability would be expected to tend towards the theoretical value. If it does not, then either the experimental methodology or the theoretical calculation and its assumptions would be questioned. In the case of the dice, the most likely explanation would be that the dice in the experiment were not truly unbiased.[1]
In situations more complex than a pair of dice, the basis of theoretical probability calculations can become increasingly contentious, and the approach tends to be useful only in the case of limited or absent observational data. For example, when the outbreak of a new strain of influenza occurs, theoretical calculations of expected mortality rates are quickly produced based on extrapolation from other flu strains. These are soon superseded by actual mortality observations.[2]
Probabilistic thinking
See also: Probabilistic thinking
Probabilistic thinking is a way of approaching situations and potential problems by considering the likelihood of a different range of outcomes on a continuum rather than assuming a single, certain outcome. It involves acknowledging uncertainty, estimating probabilities, and making sound and wise decisions based on expected value. This approach can lead to more informed decisions and better management of risk in various aspects of life.[3]
Rational thinking employs probabilistic thinking. The opposite of probabilistic thinking is binary/all-or-nothing thinking, having an optimism bias such as having a "lottery mentality" or being a pessimist who engages in negative thinking (See: Cognitive bias).
Theory of Evolution
The famous mathematician Sir Frederick Hoyle once calculated that the chances of life occurring (i.e. the theoretical probability) by chance are 1 in 10 to the power of 50, or 1 in 100,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000,000.
History
Blaise Pascal and Pierre de Fermat are credited with initially developing the field of probability in the mid-1600s,[4] but to this day the field remains relatively new compared with other fields of mathematics.
