PSYCH 100A Lecture Notes - Lecture 7: Null Hypothesis, Analysis Of Variance, Scatter Plot

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Published on 28 Mar 2018
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Lecture 7
If the population means are identical (null is true), the probability of drawing a sample that
gives an F of 7.41 or larger is 0.008 (8 out of 1000)
P values less that 0.05 (p <0.05) provide evidence against the null hypothesis
Results were significant, meaning that at least one group average differs from the others
Significant F Test
ANOVA null hypothesis - predicts that all population means are equal
A significant F statistic implies that at least one pair of groups is different
o With more than two groups, a significant F is ambiguous because we don't know
which pairs of groups is driving the significant result
Pairwise (post hoc) comparisons
Examine mean differences between all possible pairs of groups
Used when researches don't have specific hypotheses about group differences prior to
study
Idea that two events tend to happen together
Correlation (r )describes associations/trends between two continuous variables
Use correlation:
T tests apply to situations where we want to examine relationship between categorical
independent variable and continuous dependent variable
But correlation used to evaluate association between two continuous variables
Scatterplot
Helps visualize a correlation
Independent on horizontal, outcome on vertical
Correlation differs in direction and strength
o Can be positive or negative
o Strength of correlation ranges from weak (nonexistent) to strong (perfect)
Positive correlation
High paired with high, low paired with low
Negative correlation
High paired with low, low paired with high
Pearson's Correlation
Denoted r
o Quantifies magnitude of correlation on a 0 to 1 scale
o Slope of the line
Not a percentage, not a ratio scale
Correlation of 0.30 not twice as strong as 0.15
Correlation does not imply causation
o Zero correlation may/may not imply lack of association
o Can't quantify nonlinear relations
No correlation DOES NOT mean independent or unrelated variables
There are nonlinear relationships
Outliers (extreme scores)
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Document Summary

Pairwise (post hoc) comparisons: examine mean differences between all possible pairs of groups, used when researches don"t have specific hypotheses about group differences prior to study. Idea that two events tend to happen together: correlation (r )describes associations/trends between two continuous variables. Use correlation: t tests apply to situations where we want to examine relationship between categorical independent variable and continuous dependent variable, but correlation used to evaluate association between two continuous variables. Scatterplot: helps visualize a correlation, correlation differs in direction and strength. Independent on horizontal, outcome on vertical: can be positive or negative, strength of correlation ranges from weak (nonexistent) to strong (perfect) Positive correlation: high paired with high, low paired with low. Negative correlation: high paired with low, low paired with high. Pearson"s correlation: denoted r, quantifies magnitude of correlation on a 0 to 1 scale, slope of the line. Outliers (extreme scores: have substantial impact on correlation, can increase/decrease depending on location of outlier.

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