Lecture 3 Flashcards
Parameters represent effects
- Relationships between variables
- Differences between means
All parameters have
An associated sampling distribution
For any parameter, we can work out the probability of getting at least the value we have if
The null hypothesis is true
P < 0.05 is typically used as a threshold for
Significance
The p value is
The probability of getting a test statistic at least as big as the one you have observed given that the null hypothesis is true
The p value is not
- The probability of a chance result
- The probability that H1 is true
- The probability that H0 is true
What’s a type 1 error
- Rejecting the null when it’s true
- Believing in effects that don’t exist
What’s a type 2 error
- Accepting the null when it’s false
- Not believing in effects that do exist
What is statistical power
- The probability of a test avoiding a type 2 error
- The probability of rejecting H0 when H1 is true
Problems with NHST
- Tells us nothing about the importance because p depends on a sample size
- Provides little evidence about the null hypothesis
- Encourages all or nothing thinking
- Based on long run probabilities
When should you reject the null hypothesis
If p is less than or equal to the agreed significance level (usually 0.05)
When should you accept the null hypothesis
If the p is greater than the agreed significance level (usually 0.05)
If ^b1 is not 0 what does that mean
There’s a relationship as there is a gradient/ slope
H0 is
- Null hypothesis
- b=0
- b1=b2
H1 is
- Alternative hypothesis
- b isn’t 0
- b1 isn’t b2
