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Statistics, Effect size, Quasi-Independent variable - Coggle Diagram
Statistics
Description(enumerative) Statistic
Nominal variable
Statistics: Frequency
Graph: Bar chart
Scale variable
Statistics: Mean; standard deviation, z-score
Histogram
Frequency count
Measure of central tendency
Measure of variability
Analytics (inferential) Statictic
Correlation
Pearson R(regression) correlation
Relationship between 2 variables
-1< x <+1
Correlation matrix
Significant level < 0.05 is significant
Can be represent via regression line on scatterplot
Compare two means from two measurement from a same smaples
Paired t test
Within-subject design/ Repeated measure design/ paired sample design
To test whether a treatment is effective or not
The value compared MUST BE same physical quantity
SIgnificant
Is the t value> larger than critical value(either positive or negative)
p less than 0.05
95% confidence interval does not cross zero
independent t test
To test whether TWO GROUPS are independent from one another
BY testing the mean from one group is statistically significant different from another
Levene's test for equality of variances is used(for equal variance or diff variance in both groups)
Is the t value> larger than critical value(either positive or negative)
95% confidence interval does not cross zero
Is the t value> larger than critical value(either positive or negative)
Effect size
Size of the effect the differences between the means
Quasi-Independent variable