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Interpreting the Data (1.5) - Coggle Diagram
Interpreting the Data (1.5)
Key Criteria
Results myst chose statistical significance
All sampling procedures were appropriate
All experimental procedures were appropriate
All measures were valid
All possible confounding variables were controlled
Calculations
__%
= (Number of times the score occurs / Total number of scores in dataset) x 100
Mean
= Sum of scores / Total number of scores
Median
= Middle number of the scores in order
Mode
= Most commonly occurring score
Range
= Highest score - lowest score
Variance
= (Mean - Score)²
Standard Deviation
= √(mean variance)
Definitions
T-test
A comparison of means in data that reveals how significant the differences are
P-value
The probability that the difference is caused by chance.
p ≤ 0.05 is statistically significant (the difference could have been caused by chance alone on five or less times in 100)
Wilcoxen signed-rank-test
A test that compares two sets of scores from the same set of participants when the data is not normally distributed
Mann-Whitney U test
Typically used for ordinal data to test whether two means are equal or not
Generalisation
A judgment about the extent to which the research findings can be applied to the population represented by the sample
Type 1 Error
When the null hypothesis is incorrectly rejected
Type 2 Error
When the null hypothesis is incorrectly kept
Scatter diagram
Shows the values of the two variables for each participant in the same sample by representing the intersection of those two values with a dot on a graph
Correlations
Pearson Product Moment Correlation
A measure of the strength of the linear relationships between two continuous variables
Spearman Correlation
A measure of two ordinal variables that uses the ranked values for each variable to examine how they change together but not necessarily at a constant rate
Positive correlation
One in which the two variables change in the same direction - that is, as one increases (or decreases), so does the other
Negative correlation
One in which the two variables change in the opposite direction - that is, as one increases the other decreases
Correlation
A statistical measure of the strength and direction of the relationship between two variables; does not show a cause-and-effect relationship, but describes the way in which variables vary in relation to each other