PSY201H5 Lecture Notes - Lecture 8: Mutual Exclusivity, Sampling Bias, Normal Distribution
Document Summary
Learning objectives: define a random sample - why do we like random samples, define and give examples of addition and multiplication rules, define and give examples of independent and mutually exclusive events. How often were scores observed: central tendencies and variance. How far apart are scores: z-scores. How many sd units from the mean were scores: normal distribution and probabilities. What"s the likelihood of observing a score under the normal curve. Inferring what the pop is based on sample. We take a guess at the pop parameter by drawing a representative sample. Using this sample, we use probability to infer the pop. Pop: 1, 2, 3, 4, ,5 ,6. Trial 1 - roll dice 1/6 --> a one --> 0. 1667. Trial 2 - roll dice 1/6 --> a two --> 0. 1667. Draw 1/52 --> a queen of heats --> 0. 0192. Draw 1/52 --> a queen of spades --> 0. 0192. Draw 1/52 --> a queen of hearts --> 0. 0192.
