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Scientific Methods in Psychology 2.docx

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Joe Kim

Research Methods in Psychology 2 Unit 1: Intro to Scientific Research Statistics: summarize, interpret, and present the data we have collected Descriptive Statistics: present info about data at a glance to give you an overall idea of the results of the experiment - summary of mean, median and mode (all data based) Histogram: graph used to report the number of times or frequency with which groups of values appear in a data set ( x axis are divide into values all bins, y axis measures the number of values in the data set that fall into a given bin, known as Frequency Frequency distribution: graph illustrating the distribution of how frequency values appear in the data set- smooth curve that connects the peak of each bar in a histogram Normal Distribution: distribution with characteristic smooth, symmetrical, bell shaped curve containing a single peak Average: common measure in the mean calculated by adding together all of the points in a data set and dividing by the number of items in the set Outliers: extreme points, distant from others in a data set Measure of Central Tendency:  Mean: average value of set data  Median: centre value in a data set when the set is arranged numerically  Mode: value that appears most frequently in the set (focuses on centre value but doesn't tell us how the other values fall around that point) Measure of Variability: second group of descriptive statistic that review the spread and distribution of a data set (small variability = data cluster around the mean) Variability: The extent to which the scores in a data set tend to vary from each other and from the mean Standard Deviation: measure of average distance of each data point from the mean - Larger the standard deviation = larger spread of data -For a given difference between means, the lower the variability, the more likely we are to attribute that difference to our IV manipulation (we can say that our manipulation is affecting the results when there is low variability not chance) -As within group variability increases, we become less able to reject the explanation that our two groups are different only by chance, and that our manipulation had no real effect -less overlap there is between groups from the data from two groups, the less likely it is that we could have obtained the difference between group means by accident - if the difference between groups is sufficiently unlikely to happen by accident, then we will conclude that the difference is actually the result of our IV manipulation Unit 2: Inferential Statistics Inferential Statistics: allow us to use results from samples to make inferences about overall, underlying populations - Eric experiment is using two populations because one is under the influence of the energy drink and one group is the general population, but if his hypothesis is wrong than the data is drawn from a single population as the energy drink has no effect - to determine this he must compare the entire date set from his control and experimental groups to determine if they come from the same or different population T- Test: compare differences between the data from the control and experimental group (applying inferential statistics to your data sheet) Research Methods in Psychology 2 - the test considers each data point from both groups to calculate the probability that both samples were drawn from a single population - the test produces a p=value which expresses his probability of getting the results he found even if his hypothesis about energy drinks are incorrect - scientists usually requires a t-test to show a p-value of less than 0.05, indication there is only a 5% probability that they could have found the observed differences between the groups purely by chance - p-value allows us to get the probability that the results would be found even if the control and experimental groups actually come from the same population Statistically Significance: difference between 2 groups is due to some true difference between the properties of the 2 groups and not simply due to random variation Unit 3: Reviewing Experimental Design Theory: test performance can be affected by external factors prior to test writing Hypothesis: students taking energy drink should show improved test performance when compared with students not drinking energy drinks Research Methods: Identified his independent variable (Mega Study drink and dependent variable (test score); used a between participants, double blind experiment to test his hypothesis Collect Data: had all his participants write the same test Analyze Data: used descriptive statistics (mean, stdev, histogram) and inferential statistics (t- tests); p = 0.44, p>0.05, no conclusion evidence to support the hypothesis that energy drinks improve test performance Report Findings: p= 0.44 > 0.05, no conclusion evidence to support the hypothesis that energy drinks improve test performance Revise Theories: group will revise the info collected Unit 4: Observational Research Observational Studies: studies where scientists observe the effect of variables they're interested in without performing a
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