PSY 138 1st EditionExam # 1 Study GuideZ-Scores: Specifies the precise location of each X value within a distribution. The sign of the z-score (+ or -) signifies whether the score is above the mean(positive) or below the mean (negative). Proportion take a look at page 585 in the book the formulas and the scores will be given to us.Shape- Will be the same as the original distribution of raw scores. (I.E. original distribution is negatively skewed then z-score will be negatively skewed.)The Mean- The z-score distribution will always have a mean of 0.Standard Deviation- the distribution of z-scores will always have a standard deviation of 1.Because all z-score distribution have the same mean and SD, the z-score distribution is called a standardized distribution.Comparing scores- You need to know the value of the mean and standard deviation.Probability- It is defined as a fraction or a proportion of all the possible outcomes.P=probabilityRandom Sample- Requires that each individual in the population has an equal chance of being selected.Sampling Replacement- To keep the probabilities from changing from one selection to the next, it is necessary to return each individual to the population before you make the next selection.Distribution of Sample Means- The collection of sample means for all the possible random samples of a particular size (n) that can be obtained from a population.How to build one:1. Select a random samples of a specific size (n) from a population.2. Calculate the sample mean3. Place the sample mean in a frequency distribution.4. Select another random sample with the same number of scores5. Calculate sample mean, then add to distributionCentral Limit Theorem: For any population with mean and standard deviation, the distribution of sample means for sample size (n) will have a mean and a standard deviation of (standard deviation/ square root of n) and will approach a normal distribution as n approaches infinity.Perfectly normal: the population from which the samples are selected is a normal distribution.The # of scores (n) in each sample is large, around 30 or more.Hypothesis Testing- A statistical procedure that uses sample data to evaluatea hypothesis about a population.Steps to take to do this: 1. State Hypotheses (H0 means null and H1 means alternative) Nullhas no effect and alternative has an effect. This point you will decide if it will be a one tailed or two tailed. One tailed-directional, two-tailed- non directional.2. Set decision Criteriaa. Alpha level (if there is none given assume its .05)b. Critical Region: Extreme values that are very unlikely to be obtain.c. Draw the graph and label. If it is two tailed divide the alphalevel by 2.3. Collect data and calculate a sample statistic4. Make a decision a. If the z-score is in the critical region we reject the nullb. If the z-score is outside the CR then we fail to reject.Type 1 Error- Reject the null hypothesis there was actually no effectType 2 Error- Failing to reject the null hypothesis when there was an effect.When it comes to finding the t-score the steps are the same except a few things. You have to find the DF which is n-1.You also have to make a table because you have to find the SS and find variance.For practice I would go through chapter 7-9 and do the sample