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PSYCH 1X03
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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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