PSM102 LECTURE

• Probability - How likely is that thing to happen
• Event - the catch-all term to refer to any specific thing happening
• The normal distribution has an area under its curve that is equal 1
• The smaller section is called the tail
• The bigger section is called the body
• True - the body is always bigger than the tail
• Sampling Distribution - refers to probability distribution statistics that comes from choosing random samples of given population. Also known as finite-sample distribution.
• Sampling Distribution of Sample Means - method shows a normal distribution where the middle is the mean of sampling distribution
• Standard Error - the spread of sampling distribution; the quantification of sampling error, drenoted sdm
• Central Limit Theorem - States that for samples of a single size n, drawn from a population with a given mean and variance, the sampling distribution of sample means will have a mean and variance = variance/n.
• Law of Large Numbers - simply states that as our sample size increases, the probability that our sample mean is an accurate representation of the true population mean also increase.
• Observe Effect - the numerator (M-u); it is what we observe in our sample mean versus what we expected based on the population from which that sample mean was calculated
• Hypothesis - a prediction that is tested in a research study
• Probability Value - the probability of an outcome given the hypothesis
• Null Hypothesis - apparent effect is due to chance, written H0
• Alpha - probability value below which the null hypothesis is rejected. a.k.a. significance level
• a=0.05; z > 0.05

  reject null
• a= 0.05; z < 0.05

  Fail to reject null
• Type I Error - occurs when significance test results in the rejection of a true H0
• Type 2 Error - failing to reject a false null hypothesis
• A type 2 error can only occur if the null hypothesis is false.
• If null hypothesis is false, then the probability of a type 2 error is called beta
• Statistical Power - the probability of correctly rejecting a false null hypothesis = 1- beta
• t-Statistic - the ratio of the difference in number's estimated value from its assumed value to its standard error
• Confidence Interval - Starts with our point estimate and then creates a range of scores considered plausible based on our sd, our sample size, and the level of confidence with which we would like to estimate the parameter
• Point Estimate - a single value used to estimate a parameter.
• Margin of Error - the range of confidence interval, which extends equally in both directions away from the point estimate
• Upper Bound = M + Margin of Error

  Lower Bound = M - Margin of Error
• Difference Score - an index of dissimilarity or change between observations from the same individual across time
• Matched Pairs Data - a data where people who are matched or paired in some way agree on specific topic
• Paired Sample t Test - Compare two sets of measurements taken at different times by the same individuals
• Independent Samples T-Test - compares two groups that have not been matched
• Repeated Measures Design - when participants are measured more than once over time