6] Hypothesis Testing 2 Flashcards
What is sampling variation
Samples will vary because they contain different members of the population
What is sampling error
The difference between a sample statistic and the corresponding population parameter
What is sampling distribution of the mean
It is the probability distribution of all possible sample means of a given sample size
What is the central limit theorem
What is a standard error
It is the standard amount of error in each sample mean, allowing us to estimate how far each individual sample mean is from the true population
How does standard error work
It is when you calculate the standard deviation of a set of mean scores, rather than individual scores
How do we calculate the standard error of the mean
Formula of standard error
SD/ (square root) N = SE
SE (standard error of sampling distribution)
SD (standard deviation of original population scores)
N (the size of the samples you are taking)
How do we calculate the standard error of the mean for large samples
s/ (square root) N = SE
SE (standard error of sampling distribution)
s (standard deviation of sample scores)
N (the size of the samples you are taking)
Sampling distributions and errors
As sample sizes get larger the standard error (standard deviation of the sampling distribution) gets smaller
Small standard error = more trusting generalisations from sample to population
What are the two forms of hypotheses
1] Directional (one tailed hypotheses)
2] Non-Directional (two-tailed hypotheses)
What is a non-directional hypothesis
It states that there is a difference between groups, or a relationship between variables
But does not state the direction of the difference/relationship
E.g: Level of exam related anxiety will be different for 1st years compared to final year students
What is a directional hypothesis
It states that there is a difference between groups, or a relationship between variables
But specifies the direction of the difference/relationship
E.g: 1st year students will have higher levels of exam related anxiety than final year students
Hypotheses testing errors: Misinterpretation of the p-value is..
It does not represent the probability that the null hypothesis is true (or false)
Hypotheses testing errors: Correct interpretation of the p-value is..
The probability that an effect at lease as extreme would occur in a sample of the population, if the null hypothesis were true for the population
What are the two types of hypothesis testing errors
1] Type I error
Reject the null hypothesis when it is true (p>.05 but null is false)
2] Type II error
Failing to reject the null hypothesis when it is false (p<.05 but null is true)
What is an example of the two types of hypothesis testing errors
1] Type I error (false positive)
E.g: Telling an old man he is pregnant
2] Type II error (false negative)
E.g: Telling a pregnant lady she is not pregnant
How do we reduce the chance of making a Type I error
We can reduce our alpha level for statical significance like for example from 0.05 to 0.01
How do we reduce the chance of making a Type II error
We can increase our sample size so that it will increase the power of the test to detect a significant effect
