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Statistics for Psychology

40 cards · 🇺🇸 English

The statistical concepts and tests psychologists use to analyze data.

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Descriptive statistics

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  1. 1Descriptive statistics
    Statistics that summarize and describe the features of a dataset, such as mean, median, mode, and standard deviation
  2. 2Inferential statistics
    Statistics that allow researchers to make inferences and draw conclusions about a population based on sample data
  3. 3Mean
    The arithmetic average of a set of numbers, calculated by summing all values and dividing by the number of values
  4. 4Median
    The middle value in a dataset when values are arranged in order; less affected by extreme values than the mean
  5. 5Mode
    The most frequently occurring value in a dataset
  6. 6Range
    The difference between the highest and lowest values in a dataset, indicating the spread of data
  7. 7Variance
    A measure of how spread out the values in a dataset are from the mean, calculated as the average of squared deviations
  8. 8Standard deviation
    The square root of variance; a measure of the average distance of data points from the mean
  9. 9Normal distribution
    A symmetrical, bell-shaped distribution where most values cluster around the mean, with fewer values at the extremes
  10. 10Skewness
    A measure of the asymmetry of a distribution; positive skew has a tail to the right, negative skew has a tail to the left
  11. 11Z-score
    A standardized score indicating how many standard deviations a value is from the mean; allows comparison across different scales
  12. 1268-95-99.7 rule in a normal distribution
    Approximately 68% of data falls within 1 SD of the mean, 95% within 2 SDs, and 99.7% within 3 SDs
  13. 13T-test
    A statistical test used to compare the means of two groups to determine if they are significantly different
  14. 14Independent samples t-test
    A t-test comparing the means of two separate, unrelated groups
  15. 15Paired samples t-test
    A t-test comparing the means of the same group measured at two different times or under two different conditions
  16. 16ANOVA (Analysis of Variance)
    A statistical test used to compare the means of three or more groups simultaneously
  17. 17Chi-square test
    A statistical test used to determine if there is a significant association between two categorical variables
  18. 18P-value
    The probability that the observed results occurred by chance; a p-value less than 0.05 is typically considered statistically significant
  19. 19Significance level (alpha)
    The threshold for determining statistical significance, typically set at 0.05 (5%)
  20. 20Confidence interval
    A range of values that is likely to contain the true population parameter with a certain level of confidence (e.g., 95%)
  21. 21Effect size
    A measure of the magnitude or strength of a relationship or difference, independent of sample size
  22. 22Cohen's d
    A measure of effect size for the difference between two means, expressed in standard deviation units
  23. 23Statistical power
    The probability that a statistical test will detect an effect when there is a true effect; typically set at 0.80 or 80%
  24. 24What factors increase statistical power?
    Larger sample size, larger effect size, higher significance level (alpha), and lower variability in the data
  25. 25Regression analysis
    A statistical method for examining the relationship between one dependent variable and one or more independent variables
  26. 26Simple linear regression
    Regression with one independent variable predicting one dependent variable, represented by a straight line
  27. 27Multiple regression
    Regression with two or more independent variables predicting one dependent variable
  28. 28Coefficient of determination (R²)
    A measure of how much variance in the dependent variable is explained by the independent variable(s); ranges from 0 to 1
  29. 29Correlation coefficient (Pearson's r)
    A measure of the linear relationship between two continuous variables, ranging from -1 to +1
  30. 30A correlation of +1
    A perfect positive linear relationship - as one variable increases, the other increases proportionally

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