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An overview of statistics, focusing on descriptive and inferential statistics, z-scores, measures of central tendency and variability, probability, and hypothesis testing. Topics include functions of statistics, descriptive statistics, inferential statistics, quantitative and qualitative variables, measures of central tendency, measures of variability, z-distribution, standard normal curve, sampling distribution for the mean, z-table, central limit theorem, standard error of the mean, probability concepts, representative samples, directional and nondirectional tests, parametric and non-parametric tests, null and alternative hypotheses, type i and ii errors, assumptions of a one-sample t-test, and scales of measurement.

Typology: Exams

2023/2024

1 / 7

Download Psychology Statistics: Descriptive, Inferential Stats, Z-Scores, and Hypothesis Testing and more Exams Psychology in PDF only on Docsity! stats of psych midterm functions of statistics - -to communicate the results of a study -to organize data to see patterns in the data -to help people make sense out of data descriptive statistics - -organize and summarize data -puts large amounts of data into a more manageable form inferential statistics - -determine if sample data accurately represents the relationship in the population quantitative variable - scores reflect the amount of the variable that is present qualitative variable - represents the quality or category that is present sample statistic - the mean for a sample calculated bar chart - type of graph used for discrete or qualitative data measures of central tendency - -median: the middle value -mode: the number that has highest frequency -mean: the average measures of variability - statistics that summarize the extent to which scores in a distribution differ from one another z-score - a statistic that tells us how far a score is from the mean in the standard deviation units 3 characteristics of z-distribution - -it always has the same shapes as the raw score distribution -the mean of any z-distribution is 0 -the standardized deviation of any z-distribution is 1 standard normal curve - a perfect normal curve that serves as a model of an approximately normal z-distribution sampling distribution for the mean - is a distribution of all sample means that could be obtained in samples of a given size from the same population z-table - shows the proportion of the scores above/below/between any z-score(s) central limit theorem states - the mean of a sampling distribution equals the mean of the underlying raw score population used to create that distribution standard error of the mean - -examples: standardized scores, temperature (except Kelvin), and Likert scales -continuous -quantitative -(e.g. rating scale score, temperature in Fahrenheit) ratio scale of measurement - -highest information given -can have fractions and decimals -has absolute zero -can go negative -always uses mean -quantitative -continuous -(e.g. time of day, number of students in your class, time (in minutes) to prepare dinner) N - number of scores in the data f - frequency Ex - sum of X Xbar - sample mean u - mu; population mean Ex2 - sum of squared x's (Ex)2 - squared sum of X S2x - sample variance Sx - sample standard deviation Ox - population standard deviation z - z-score Oxbar - standard error of the mean p - probability Ha - alternative hypothesis Ho - null hypothesis Zobt - obtained value of z-test Zcrit - critical value of z-test a - -alpha -theoretical probability of making a Type I Error Sxbar - estimated standard error of the mean df - degrees of freedom Tobt - obtained value of t-test Tcrit - critical value of t-test