Classical theory of probability is basically used to define probability of equally likely events. Normally, events classified under this category are referred to as ‘equally possible’. Therefore, the probability is the total number of possible cases over the total number of relevant cases. Alternatively, classical theory terms the probability of an event as a proportion of alternatives amongst possible alternatives in a given situation. Classical definition is also referred to as priori which implies that it is based on logic without any observed facts. Otherwise, it is based on past studies which cannot be mathematically challenged. For instance, in tossing a die, one knows that six is the total number of likely outcomes, in such a case, experiments are unnecessary.
There must be an equal likelihood of events occurring such that the ratio can be valid. A common example is tossing a die. In such a case, the probability of rolling a 5 is a sixth. Similarly, tossing a coin involves an equal likelihood of ether getting a head or tail.
Practical applications are minimal because classical probability on statistics that can be easily determined, additionally, there must be an equal likelihood of events for the validation of classical probability. Classical probability differs slightly from empirical probability in that in empirical, the experiments has to be performed whereas in classical no experiment is necessary. However, classical theory of classification has some applications as highlighted alongside. The concept of classical probability is the estimation of dollar value, evaluation of basic quantities as well as the determination of the characteristics of a given universe.
Empirical probability on the other hand, refers to experimental probability. This is actually the ratio of the total number of favorable outcomes to the total number of trials made. Empirical probability is basically dependent on the assumption that two experiments or rather occurrences are independent. This implies that the outcome of one occurrence is independent of other available occurrences. A good example is an experimental evaluation of the probability that out of a given number of men (9), 3 men love soccer and that 4 of the total number of men have families. The probability is thus a fraction of men that love soccer, which is a third, multiplied by four over nine. Thus the probability that men love soccer and have families out of the given sample space is four over twenty seven (4/27).
Statistical models have been put forth by certain remarkable scholars, for instance the binomials distribution, which gives an estimate of the maximum likelihood. On the other hand, Bayes theorem involves certain assumptions for a prior distribution of the probability.
Since minimal or no assumptions are involved, empirical probability is involved, is widely used in a wide range of fields for instance, in quality control of industrial products using. Using statistical records of any given product, it is basically very easy to compute the probability of the projected quality of new commodities being manufactured,
Another field of empirical probability is weather forecasting. Use of reliable and adequate samples can effectively be used to determine the expected outcomes on the single line of statistics. Empirical statistics are mostly used in academics in elementary levels, to explain basic concepts of probability due to their high level of precision and logicality.
Subjective probability is characterized by personal judgment. There are no mathematical calculations necessary in determining the outcome. Obviously, such probabilities vary from one individual to another depending on their experiences and perspectives. A good example of subjective probability is predictions that are made on outcomes of football tournaments, World cup, for example. On such predictions, bets are made depending on the faith that each individual has on the winning capability of the teams involved
The major factors that determine the probability of an event occurring are intelligence and experience. Intelligence involves one’s perspectives and ability to observe various occurrences. Maintaining the record over a given period of time, enable one to evaluate factors that are necessary for high chances. Experience of an individual is utterly relevant in the determination of the required probability. The involved individual depends on his or her own experience in predicting the outcome. Based on certain factors necessary for a positive outcome, an experienced person can evaluate them and be able to tell the possible outcomes. For example, in football, one can precisely analyze the combination of the players and be able to determine the outcome of the game. Other aspects are also involved, like for instance, one may give predictions on the must basis such as, a certain team for instance, must win to qualify for the finals.
In politics, the probability of a certain aspirant winning is based on subjective probability such that the given aspirant can be able to apply his experience and intellectual ability to evaluate his or her chances. This way, some aspirants are able to determine their fate even before the voting process. Subjective probability is necessary for the purpose of improving ones tactics during the process of campaigning. Other areas of application are gambling where probabilities are termed as chances in which case, experience is the major determining factor.
