W1 Flashcards
what is power
probability that the test will correctly reject a false null hypothesis (detect an effect when there truly is one). In other words, it’s the likelihood of avoiding a Type 2 error (false negative). Power is typically influenced by sample size, effect size, significance level (α), and variability in the data.
what is type 1 error
- false positive or alpha (α) error
- you reject the null hypothesis (H₀) when it is actually true.
- It means you conclude that there is an effect or difference when in reality, there is none.
- Example: Suppose a clinical trial tests whether a new drug is effective. A Type 1 error would mean concluding that the drug works when it actually doesn’t.
- Alpha Level (α): typically set at 0.05 (or 5%). This means there’s a 5% risk of incorrectly rejecting a true null hypothesis.
what is type 2 error
(False Negative or beta (β) error)
- occurs when you fail to reject the null hypothesis (H₀) when it is actually false.
- It means you conclude that there is no effect or difference when there actually is one.
- Example: a Type 2 error would mean concluding that the drug does not work when, in fact, it is effective.
- Beta Level (β): The probability of committing a Type 2 error. The power of a test (1 - β) indicates the ability to detect a true effect when it exists.
what are the assumptions of an independent t test
- Dependent variable continuous (assumption 1)
- Calculation of SE only valid if variance of dependent variable does not differ between groups (homoscedasticity) (assumption 2) (the variance of the residuals (the differences between observed and predicted values) is constant across all levels of the independent variable(s). In other words, the spread of the data points should be roughly the same, regardless of the value of the predictor.)
- Formula for SE only applies if data of subjects are independent! (e.g. no twins) (assumption 3)
- Look up test statistic (t-value) in distribution under H0. But only valid if dependent variable is normally distributed within each group (assumption 4)
what happens when the assumption of independence of subject is not met
less varience so a smaller se which leads to a higher chance of getting significant t value, that might not be true
t=y1-y2 / se (y1-y2)
what is the difference between a t test and a linear regresion
Linear Regression is used for modeling and predicting continuous outcomes based on multiple predictors, while a t-test is used for comparing the means of two distinct groups.
what are the assumprions of a linear regression
LINEAR REGRESSION ANALYSIS
* Dependent variable continuous (assumption 1)
* Variance of residuals equal at each value of x
(homoscedasticity) (assumption 2) - scaterplot
* Data of subjects are independent! (assumption 3)
* Residuals are normally distributed (assumption 4) - pp plot
what are the assumptions of Factorial ANOVA
(FACTORIAL) ANOVA
* Dependent variable continuous (assumption 1)
* Variances of dependent variable are equal in all groups (assumption 2)
* Data of subjects are independent! (assumption 3)
* Dependent variable is normally distributed in each group (assumption 4)
General interpretation b0 and b1
- b0 = score on dependent variable (y) that you expect as independent variable (x) equals 0
- b1 = difference in score on dependent variable (y) that you expect as independent variable (x) increases by 1
what do T-test, ANOVA and linear regression have in common
they are variants of the General Linear Model (GLM)
what is a T Test
A t-test is a statistical test used to determine if there is a significant difference between the means of two groups. It evaluates whether the difference between the group means is likely due to chance or reflects a real effect.
what is a Linear regression
A statistical method that models the relationship between a continuous dependent variable and one or more independent variables (which can be continuous or categorical) to predict outcomes and assess how the independent variables affect the dependent variable.
what is a factorial ANOVA
A statistical test used to analyze the effect of two or more categorical independent variables (factors) on a continuous dependent variable, and to examine both the main effects of each factor and their interactions.
what is the difference between a factorial ANOVA a t test and a linear regression analyisis
Factorial ANOVA deals with multiple categorical factors and their interactions, while the t-test focuses on comparing two groups.
Linear Regression is more flexible, allowing continuous and categorical predictors to examine relationships and make predictions, rather than just comparing group means.
what is the trasnlational research framework
its a framework that outlines the steps required to translate scientific findings into clinical treatment
