Probability Wiki

 + Probability

Probability is the measure of our belief that something will happen. For example, there is a 1/2 chance that I will get a head when flipping a quarter. There is a 90% chance that I will go for a bike ride tomorrow morning. (Gary Parker)

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 - Theoretical Probability

Theoretical Probability is the likelihood of something that will occur. Based on knowing the complete knowledge of the subject. The number of favorable outcomes/total outcomes. For example, there is a 1/6 chance a 4 will be rolled when rolling dice. (Casandra Jensen)  

 - Empirical Probability 

Empirical probability is the ratio of the number of outcomes which a certain event occurs to the total number of trials. This usually occurs in an actual experiment not theoretical. It is based off of observation and experience. (Baylee Powers)

 - Educated Guess Probability

Educated Guess probability is the likelihood of a result based on strong research and evidence. For example, weather is usually never predicted exactly right. Meteorologist can predict that there will be 5-15 percent chance of rain, but what weatherman will say is that there is a 10 percent chance of rain, which is their educated guess. (Dylan Grogan)

 +  Minimum Probability

The minimum probability can not exceed 1 (100%). P(A or B)=P(A)+P(B)−P(A and B)   P(A or B)=P(A)+P(B)−P(A and B)

 +  Maximum Probability

The maximum probability is when the probability value cannot exceed a certain amount. For example, if the probability of an event is .4 then the maximum probability cannot exceed it. (Jesslyn Kriebs)

 +  Experiment 

      An experiment is a thought out procedure used to test a hypothesis or known event performed under set conditions. Experiments can be used        to determine if one variable (independent) causes changes to another variable (dependent). (Shannon Hatley)    

 +  Chance

   Chance is the likelihood for something that is not predetermined to happen. An example would be winning the lottery because you did not know you would win but you took a chance and put in for it. (Morgan Correa)

 +  Sample Space 

Sample Space is the set of all possible results for that experiment. All of the possible ordered outcomes are listed as elements within the set. (Jordan Schmidt)

 +  Outcome

When an experiment is all said and done, the finished results are called an outcome. Whether favorable or unfavorable. (Adriana Ruiz)

 +  Event

A subset of the set of all outcomes of an experiment (Kylee Smith)

 +  Simple Event 

A Simple Event refers to an event where one experiment is done at a time, and will have a single outcome. This is indicated by P(E) where "E" is the event. The probability will be between 0 and 1. Flipping a coin is an example of a Simple Event. However if I were to flip a coin and roll a die, this would not be a simple event, since two events are happening at the same time. (Felix Hernandez)

 +  Equally Likely 

       Equally likely means the outcome of an experiment will have the same probability. For example, if you have a bag of balls of all sizes and            colors and you would assume that that you would have an equal outcome.   (Kendall Davis)

 +  Fair 

       Fair means there is an equal chance of all outcomes. When flipping a coin, there is an equal chance of rolling a heads as there is of rolling a tails. Or on a six sided dice there is an equal chance of any number, (1, 2, 3, 4, 5, 6) being rolled.    (Riley Lankford)

 +  Independent

       Two events A and B are independent if the knowledge that one occurred does not affect the chance the other occurs. For example, the outcomes of two roles of a fair die are independent events. (Tiah Benedict)

       The first time you roll a dice the chances of rolling a four are 1 in 6. Even if you roll multiple fours in a row, you may think your chances of rolling another four have decreased because it seems so unlikely. In reality your chances of rolling another four are exactly the same because the rolls are independent. (Hanna Strate)

 +  Mutually Exclusive
         In order to be mutually exclusive, two specific events must have the probability of them both happening at the same time to be zero. The             Equation for this is P(J and K)=0. (Jennifer McDougall)

 +  Long-term Relative Frequency 

The Long-term Relative Frequency is the probability of something that is being tested repeatedly/indefinitely.  (giovanna garcia)

Example would be if you flipped a coin 20 times, 200 times, 2,000 times still the probability would be `~ ~~ ~`  0.5 (April Meadows)

 +  Law of Large Numbers 

The Law of Large Numbers states that when the size of the sample is larger or increased , the more likely the sample's mean is going to be to the population's mean (µ). For example, when rolling a dice, if you add all of the possibilities (1,2,3,4,5,6) and divide them by 2, you get the answer of 3.5 which would be the population's mean (µ). If you only roll 3 times (sample: 6,6,3) you would get a sample mean of 5. And if you were to keep rolling and getting more numbers, it would slowly get closer to the population mean (µ) of 3.5. (Siobhan Holman)

 +  OR usually implies Addition 

An outcome is in the event A OR B if the outcome is in A or is in B or is in both A and B. For example, let A= {1,2,3,4,5} and B={4,5,6,7,8}.A OR B {1,2,3,4,5,6,7,8,}.  Notice that 4 and 5 are NOT listed twice. So a real life example might be Bowl A { banana, apples  oranges} or  Bowl B {banana, apples,melon,kiwi} the outcome would be {banana,apple,orange,melon,kiwi} (April Meadows)

 +  AND usually implies Multiplication 

 +  Complementary Events 

Events where the probability of one event prevent the happening of the other event. Ex. when you toss a coin, you get heads, but not tails. So getting heads OR tails are complementary events. The sum of probabilities of all possible events is always one. When you toss a coin, the probability of getting heads is 1/2, and the probability of getting tails is 1/2 and the sum of these two events is 1. (Brenda Uselman)

So if we are given that the probability of getting heads P (Heads) = 1/2, then

Probability of getting tails P (Tails) = 1 – P (Heads) 

 +  Conditional Probability

is the likelihood that an event will occur given that another event has already occurred. The conditional probability of A given B, P(A/B), is the probability that event A will happen given event B already happened. (Mark Otton)

- False Positive

•  In medical testing, and more generally in binary classification, a false positive is an error in data reporting in which a test result improperly indicates the presence of a condition, such as a disease- the result is positive, when in reality it is not present, while a false negative is an error in which a test result. This term can also be known as a "false alarm" or a "false positive error. An example of a false positive would be a pregnancy test, the test would show that the women who took the test is pregnant when in reality she is not, the test came back as a false positive. (Jenna Doherty)

- False Negative

 This indicates that you tested negative when in reality you are positive (Yvonne Wilson)

 +  Unusual Events

An unusual even is an event with a low probability of occurring (generally below 5%, 0.05). (Giovanni Gallardo)

An example of an unusual event is Shaquille O'Neal making a 3 pointer. In his 18 year career, O'Neal shot 22 3 pointers and only made 1, meaning he has a 4.5% chance to make a 3 pointer. (Griffen Mattson)