ANOVA Difference between ANOVA and t tests Why not multiple t-tests? Application differences
Addition Rule of Probability
It simply refers to the likelihood of an event taking place whenever the occurrence of an event is uncertain. The probability of a single event can be calculated by dividing the number of successful trials of that event by the total number of trials.
Expected Value
When a large number of trials are performed for any random variable ‘X’, the predicted result is most likely the mean of all the outcomes for the random variable and it is known as expected value also known as expectation. The expected value, also known as the expectation, is denoted by: E(X).
Probability Distributions
Understanding probability is necessary to know the probability distributions. In statistics, probability is how the uncertainty of an event is measured. This event can be anything. The most common examples include tossing a coin, rolling a die, or choosing a card. Each of these events has multiple possibilities. Every such possibility is measured with the help of probability. To be more precise, the probability is used for calculating the occurrence of events that may or may not happen. Probability does not give sure results. Unless the probability of any event is 1, the different outcomes may or may not happen in real life, regardless of how less or how more their probability is.
Basic Probability
The simple definition of probability it is a chance of the occurrence of an event. It is defined in numerical form and the probability value is between 0 to 1. The probability value 0 indicates that there is no chance of that event occurring and the probability value 1 indicates that the event will occur. Sum of the probability value must be 1. The probability value is never a negative number. If it happens, then recheck the calculation.
ANOVA
- Difference between ANOVA and t tests
- Why not multiple t-tests?
- Application differences
ANOVA:
Anova is splitting up of total variance into variance due to several factors (Assignable cause) and variance due to error (Chance cause) and testing the equality of variance with the error variance.
t-TEST:
When it is small sample we can apply t-test is the most commonly applied when the test statistic would follow a normal distribution if the value of a scaling term in the test statistic were known.
| Anova | T -test |
| Anova is a statistical technique that is used to compare the means of more than two populations. | T-test is a hypothesis test that is used to compare means of two populations |
| Test statistic for Anova is | Test statistic for T-test is t= |
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