| Term | Definition (Cheat Sheet Version) | | :--- | :--- | | | Probability of seeing your data (or more extreme) IF the null hypothesis is true. | | Alpha (α) | The threshold for significance (usually .05). The risk of a Type I error. | | Type I Error | False positive. Saying there is an effect when there isn't (p < .05 by chance). | | Type II Error | False negative. Missing a real effect (usually due to low power). | | Power (1-β) | Probability of correctly rejecting a false null. (Target > .80). | | Effect Size (d) | .2 = Small, .5 = Medium, .8 = Large. (The "wow" factor, not reliant on sample size). | | Skew | Asymmetry. Positive skew = tail to the right (Mean > Median). Negative skew = tail to the left. | | Kurtosis | Tails. Leptokurtic = Fat tails (outliers). Platykurtic = Thin tails. | | Homogeneity of Variance | The spread of scores is roughly equal across groups (required for t-test/ANOVA). | | Outlier | A score > 1.5 IQR (Interquartile Range) from the box. |
The STAT1103 cheat sheet is more than a permit to bring notes into an exam. It is a in a subject that often feels like a foreign language. When crafted with care, it transforms panic into procedure. stat1103 cheat sheet
When in doubt, visualize it.
In the high-stakes world of university statistics courses—where variables are independent but students rarely feel that way—one humble document stands between chaos and clarity. For thousands of students enrolled in at institutions like the University of Queensland and beyond, the "cheat sheet" is not just allowed. It is essential. | Term | Definition (Cheat Sheet Version) |
Used to count the number of occurrences over a specific interval of time or space (e.g., customers arriving per hour). | | Type I Error | False positive
Preparing a cheat sheet is about more than just listing formulas; it's about creating a decision-making framework for research design and data analysis. This course, primarily offered at Macquarie University , bridges the gap between psychological theory and empirical data using Stata . 1. Variables and Measurement Levels