W3 Sampling Error and Hypothesis Testing

Sampling Error

The discrepancy, or amount of error that exists between a sample mean and the corresponding population mean

Sampling Distribution

The degree of variability between samples that we can expect happened by chance. The probability that a particular sample mean comes from this population.

Standard error

Standard deviation of the distribution of sample means, the difference between the population mean and the sample mean we would expect by chance.

Null Hypothesis

states that there is no difference between groups or no relationship between variables. A presumption of status quo or no change.

Alternative Hypothesis

There is a difference between the individual mean and the population mean.

Critical Region/Value

The area with significant scores that we are interested in. α = alpha = critical region (0.05 in psychology)

Variability due to chance

Sample to sample variability that is expected

Type I error

Erroneously rejecting a true null hypothesis, saying that there is a significant difference when there is not.

Type II error

Failing to reject a false null hypothesis, saying that there is no significant difference when there actually is.

One tailed test

We make a prediction as to the direction of the difference, for example we expect this person to be significantly higher than the population mean. This means that we live with a 5% error rate, (α) alpha level.

Two tailed test

No prediction is made to the direction, for example we expect there will be a significant difference between the two samples. We don’t state which mean will be higher

Rejection/Significance level

0.05 is the cut off in psychology. This also produces a 5% chance of making a type I error when we reject the null hypothesis