Random Samples
Everyone in the population has an equal chance of being picked
-Independent means picking one person doesn’t affect the chances of others being chosen
Non-Random Samples
1.) Convenience Sampling-Participants that are readily available/easy to contact
2.)Self Selection-When participants choose to participate in the study
Probability is used to….
Predict the type of samples that are likely to come from a population
Mutually Exclusive Events
The occurrence of one outcome prevents the occurrence of another (2 or more events can’t occur at the same time)
Unions/Disjunctions
At least one of a number of possible events occurs
-either A or B or both
Intersections/Conjunctions
Two or more events occur at the same time
-A and B
Conditional Probabilities
The probability that one event (A) will occur, given that another event (B) has occurred
Z Score to Raw Score
X= z times standard deviation plus mean
Raw Score to Z Score
Z= (raw score - mean)/standard deviation
Binomial Distribution
A series of observations where there are 2 possible outcomes and the probability is independent (heads or tails in a coin flip)
Mean for Binomial Distribution
μ=n×p
-n is number of trials
-p is probability of success for each trial
-gives you expected number of successes
Standard Deviation for Binomial Distribution
σ = sqrt(npq)
When does a Binomial Distribution approximate a normal distribution
-When the sample size is big enough
-When the probabilities aren’t too extreme
When does the binomial distribution approximate a normal distribution
-P times sample and Q times sample ad the result has to be greater than or equal to 10
