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Chapter 1&2

Chapter 1 & 2 WINTER 2014 -- PS296.docx

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Max Gwynn

Chapter 1: Intro Statistics – set of procedures and rules for reducing large masses of data to manageable proportions & for allowing us to draw conclusions from those data Take statistics to be able to analyze the results of experimental research 1.1 – A Changing Field researchers in behavioural sciences concerned with whether a difference that they found b/w experimental groups (or relationship between 2+ variables) was reliable field slowly changed by asking if a difference was meaningful effect size – number of different indices of importance combining studies dealing with a particular thing – meta-analysis 1.2 – Importance of the Context common experimental task demonstrating morphine tolerance – placing rat on a uncomfortably warm surface latency of paw-lick is the measure of rat’s sensitivity to pain Siegel’s Morphine Study Conditioned (learned) responses Unconditioned (natural) responses Theorized – administering a series of pre trials  develop morphine tolerance Used the compensatory mechanism Concluded – drug overdoses occur in novel settings 1.3 – Basic Terms statistical procedures = descriptive & inferential statistics Descriptive Statistics Describing a set of data EX. Average length of time it takes a normal mouse to lick its paw when placed on warm surface Time it takes a morphine-injected mouse to do same thing Amount of change in latency of paw-licks Crime rates, dieting scored on Eating Restraint Scale, summary info concerning exam grades in a course Inferential Statistics Inferring characteristics of populations (parameters) from characteristics of samples (statistics) Generalizing from single observations Used when studying something that has very little variability Difference b/w how we determine the # of legs on a cow VS. the milk production of cows depends on variability (would need a herd of cows to measure how much milk a cow will produce, every cow is different) Must draw sample from population Populations, Samples, Parameters, Stats Population – entire/complete set of events in which you are interested in Can range from a relatively small set of #’s – easily collected, to an infinitely large set of #’s – can never be collected completely Usually interested in large populations Sample – a subset from a population of events interested in Used to infer something about the characteristics of the population Compute numerical values Statistics – numerical values summarizing sample data Parameters – numerical values summarizing population data Random Sample – sample which each member of population has equal chance of inclusion True random sample – estimate parameters of population & get good idea of accuracy of our estimates A sample that is not random is meaningless – may no accurately reflect entire population Relevant Population – collection of numbers from which the sample has been randomly drawn Inference Taking 2 different samples of mice and testing them, one sample mean would be larger than another Must make statistical inferences from a sample to a population, then must make a logical inference Want to know whether a difference we find is unlikely to be due to chance, but also want to know how meaningful the difference is Meta-analysis – drawing conclusions from a WHOLE set of similar experiments on a given phenomenon 1.4 – Selection among Statistical Procedures we must describe a set of data before making inferences Decision Tree – scheme used selecting among the available statistical procedures to be presented in the text Graphical representation of decisions involved in the choice of statistical procedures Located on the inside of the back cover of text Types of Data Numerical Data Measurement Data (Quantitative) – data obtained by measuring objects/events Score on a measure of stress, person’s weight, speed @ which person can read a page Categorical Data (frequency/count data) – data representing counts or number of observations in each category There were 238 votes for the new curriculum and 118 against it Obtaining a latency score for each mouse (measurement data) Classifying the mice as showing long, medium, short latencies, then counting umber in each category (categorical data) Differences Vs. Relationships Most statistical questions fall into 2 overlapping categories Differences & relationships Number of Groups or Variables Obvious distinction between statistical techniques concerns the # of groups/# of variables What is referred to as an independent t test – restricted to the case of data from @ most 2 groups of subjects Analysis of variance – applicable to any number of groups 1.5 – Using Computers most calculations now done by computers simple procedures – formulae important in defining
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