Possibility vs. Probability

Either for predicting possibility or evaluating probability, statistics provides a data-driven, fact-based approach.

Uncertainty is only certain and the best opportunity in organizations or any field to explore the "art of possibility." If there is no uncertainty, then there will be a monopoly. Probability and possibility are both used to talk about how likely something is to happen, but they have distinct meanings.

Possibility simply refers to whether something is conceivable or not. It doesn't say anything about how likely it is to occur. Traditional statistics follow a frequentist approach, where the probability of an event is based on the frequency of its occurrence in a long run of repeated trials. From a statistics perspective, possibility isn't expressed as a single number like you might see in probability. Statistics deals more with the likelihood of events happening based on data analysis.

Possibility: Focuses on existence: Broader scope: Almost anything can be considered a possibility, even if it seems highly unlikely. So use statistics methodology to assess the possibility. Probability Distributions: Statistics employs various probability distributions (like normal distribution) to model the likelihood of different outcomes for a random variable. This helps us understand the range of possibilities and how probable each outcome is within that range.

Frequency: We look at how often something has happened in the past based on available data. This gives us an idea of how probable it is for something similar to occur again.

Confidence Intervals: Statistics uses calculations to create a range of values where the true value is likely to fall within a certain level of certainty (usually 95% confidence interval is common). This helps express possibility with a range rather than a single number.

Hypothesis Testing: Use statistical tests to assess the likelihood of a particular hypothesis being true. By analyzing data, you can determine if the evidence supports the possibility that a hypothesis is true or if it's likely false.

Probability: Focuses on likelihood: Probability deals with how probable something is to happen. It's expressed as a numerical value between 0 (impossible) and 1 (certain). Quantifiable: Probability uses math and data to assign a likelihood to an event. Estimating Probability from Frequency Approach: This is the most common way to view probability from a statistical perspective. Long-Run Frequencies: Statistics is concerned with what happens in the long run. Even though the next coin flip could be heads or tails, in the long run, we expect heads and tails to appear with roughly equal probability. This is the foundation of many statistical tests.

Sampling and Randomness: Statistical methods often rely on collecting data from a random sample of a larger population. Probability theory helps us understand how well this sample represents the whole population and allows us to quantify the margin of error in our estimates.

 Possibility                                                             Probability

Whether something can exist                                     How likely something is to happen

Broader (almost anything can be possible)           Narrower (focuses on likelihood)

Quantification Not quantifiable                       Quantifiable (uses numbers between 0 and 1)

Let’s imagine “the art of possible.” If the decision-making scenario is well-designed and well-executed, you have the highest probability of getting the best outcome in the state of knowledge accessible at the time of decision-making. Either for predicting possibility or evaluating probability, statistics provides a data-driven, fact-based approach. It doesn't guarantee an outcome, but it helps us quantify the likelihood of something happening based on past observations and patterns.

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