PSY111 Quiz 2

Simple random sampling is the most commonly used method in the social sciences.

This type of error refers to rejecting a null hypothesis that is in fact true (also called a False Positive):

p < 0.05 means the chance of your results being what they are if the null hypothesis was true is less than:

In a normal distribution, approximately what percentage of scores will be within 2 standard deviations of the mean

This sampling procedure is used to sample from hard to reach or hidden populations:

This type of error refers to failing to reject a null hypothesis that is in fact false (A False Negative):

This sampling method involves drawing participants from a number of strata (sub-populations) that are preselected:

This sampling procedure involves drawing samples from populations that are readily available to the researcher:

This probability distribution is used with continuous variables and is defined by the mean and standard deviation.

What does it mean when something is "statistically significant"?

In inferential statistics, this is what you are drawing inferences about (it is abstract):

According to the law of large numbers, as samples get larger they are less like the populations they are drawn from:

Socioeconomic status (low, middle, high) has how many degrees of freedom?

Non-parametric tests assume that the data follows a normal distribution.

This branch of statistics help us draw conclusions from a sample about a larger population.

What does an assumption of independence mean?

In a probability distribution, the probability of the events must add to 2 (this is called the law of total probability)

This is the branch of mathematics that tells you how often different kinds of events will happen:

The critical value is what you get when you run a statistical analysis (i.e., the result of the math behind the test).

In statistics, these are extrapolated from your sample and are the characteristics of the population:

If you are examining group differences between more than two groups from the same participants you use:

X²(1) = 0.34, p = 0.12, is a statistically significant result:

This distribution is used with categorical data and is heavily skewed towards the low end.

When do you use a X² test?

Unlike statistics, probability starts with a known model of the world and use that knowledge to do some calculations.

If you are looking to examine the relationship between more than 2 variables, you would use this statistical test:

This distribution looks familiar to a normal distribution, but the tails of the curve extend further outwards.

I am examining the relationship between gender (female, male, nonbinary) and smoking status (yes/no), which would I use?

This is a sampling procedure in which every member of the population has the same chance of being selected:

This distribution is used with discrete variables that have only 2 response options.

The McNemar test is used when you are working with nominal variables with too few counts / small samples.

If you are looking to compare two different groups of participants, you would use:

If you are looking to examine the relationship between two variables, you would use this statistical test:

This test is used to compare an observed frequency with an expected frequency when all assumptions are met.

X²(1) = 0.34, p = 0.05, is a statistically significant result:

This statistical test is used when you are examining frequencies.

The type of probability distribution you use is determined by the nature o the variable (e.g., discrete vs continuous)

We are looking to reject or retain this hypothesis; states there is no relationship or no group difference:

Probability distributions are used to determine the likelihood of a variable having a specific value:

An effect size tells us how big an association/deviation is; it helps us gauge the importance of a finding:

In statistics, these are drawn from your data and are the characteristics of your sample:

This is used to examine the relationship between two nominal/categorical variables when all assumptions are met.

Statistical significance means that the findings of your research are important:

In inferential statistics, this is what we are drawing inferences from (it is concrete):

What does a p-value represent?