Understanding probability theory and its terminology
the heads and the tails. If we consider an experiment of tossing two dice, on the other hand, our sample space will have 36 possible outcomes. In the aforementioned experiments, the outcomes can have either a discrete or continuous random variable. And what is a random variable, you may ask? A random variable is observed when the outcome has numerical values. They are of two types:
- Discrete random variable: A discrete random variable is a variable that takes on a small number of distinct values like 0, 1, 2, 3, 4, 5…
- Continuous random variable: This one takes a limitless number of distinct values. Measurements such as length often fall under continuous random variables.
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Real-life applications of probability theory
- Weather forecasting: The meteorology department can’t forecast exactly what the atmospheric conditions will be without probability. They have to use instruments and tools to determine the chances that there will be sunshine, rain, or snow. If 40 out of 100 days experience rain, for instance, then there is a 40% likelihood of rain. Meteorologists also use past data to estimate how high or low the temperatures will be in the future and other probable weather patterns.
- Insurance options: Probability theory plays a significant role in determining the best insurance policies for you and your family. For example, when selecting a motor insurance policy, you can use probability to analyze the likelihood that you will require to file a claim. For instance, if 20 out of 100 drivers (20% of drivers) in your area have hit a person over the past few months, you will likely consider a comprehensive cover for your car, not just liability.
- Gaming: If you are playing a game that involves luck or chance, you will use probability to decide the most appropriate move. For instance, if you are placing a bet on the football team, you may want to consider how many times the team you want to bet on has won the match over the past year.