Objective Probability: What it is, How it Works, Examples

What Is Objective Probability?

Objective probability refers to the chances or the odds that an event will occur based on the analysis of concrete measures rather than hunches or guesswork. Each measure is a recorded observation, a hard fact, or part of a long history of collected data. The probability estimate is computed using mathematical equations that manipulate the data to determine the likelihood of an independent event occurring. An independent event is an event whose outcome is not influenced by prior events. Subjective probability, by contrast, may utilize some method of data analysis but also uses guesstimates or intuition to determine the chances of a specific outcome.

Objective vs. Subjective Probability

Objective probabilities are a more accurate way to determine the probability of a given outcome than subjective probability That's because subjective probability is largely based on human judgment and experiences. Objective probability, on the other hand, allows the observer to gain insight from historical data and then assess the likelihood of a given outcome.

Subjective probability allows the observer to gain insight by referencing things they have learned and their own experience. Rather than being derived solely from hard data and facts, subjective probability is largely based on a person's estimate or intuition about a situation and the likely outcome.

Objective probability is based on empirical evidence using statistics, experiments, and mathematical measurements rather than relying on things like anecdotes, personal experience, educated guesses, or hunches. In the financial world, using objective probability is particularly important in order to avoid the mistake of making emotional decisions when investing.

It is true that individual investors often rely on hunches, rules of thumb, or old wive's tales to justify making the particular investment that too much relies on subjective matters and emotional influence. Objective probability rids you of the emotional and anecdotal aspects of evaluating outcomes.

Examples of Objective Probability

One could determine the objective probability that a coin will land "heads" up by flipping it 100 times and recording each observation. This would likely yield an observation that the coin landed on "heads" approximately 50% of the time, which is an example of a purely objective probability.

An example of subjective probability is when a person who is educated about weather patterns examines things such as barometric pressure, wind shear, and ocean temperature, then predicts the likelihood that a hurricane will head in a certain direction based on their previous experience. While the data aids in the decision-making, the ultimate prediction is based on probabilities that have been guesstimated by the weather forecaster.

When judging probabilities—or performing any statistical analysis—it is important for each observation to be an independent event that has not been subject to manipulation. The less biased each observation is, the less biased the end probability will be. That's why many prefer objective over subjective probabilities because it leaves less room for emotions or biases to seep into the process, as numbers, hard facts, and models replace guesswork, hunches, and intuition.