Understating Probability for inferential statistics
Probability :
- Probability is a chance of getting success and failure, those are represented in fractions and percentages.
- Example: A coin is tossed, it can be categorized preferred outcomes(1) and possible outcomes(2)
Expected Values :
- An expected value can be an average outcome that we expect when we run our experiment many times.
- Experiment: it refers to multiple trials like tossing 50 coins and getting 50 outcomes are considered as single experiments. These are known as experimental probabilities. Experimental probabilities are easy to compute.
- Experimental probabilities are a good predictor for theoretical probabilities
Probability Frequency Distribution :
- A collection of probabilities for each possible outcome.
- To obtain probability frequency distribution we can divide the frequency by the size of the sample space.
- This can be represented through a graph or table.
- The highest value is considered as the Expected Value.
Complements :
- Every event has complements(A’).
- Complements are always mutually exclusive.
- If a set consists of all odd numbers then its complement would be set of all even numbers.
Notation :
