Module 5

Question 1

descriptive statistics

Answer 1

• to describe data
• they are statistical procedures which are used to organize, summarize, and simplify data

Question 2

inferential statistics

Answer 2

• to make inferences
  -enables us to generalize or male inferences from a sample of data to a larger group of subjects or a population
• allows us to go beyond the data to generalize to the larger population

Question 3

what are the two broad areas of statistical inferences?

Answer 3

• estimation of population parameters
• hypothesis testing

Question 4

what is estimation of population parameters

Answer 4

used to estimate population parameters such as means and proportions

Question 5

what is hypothesis testing?

Answer 5

-used to examine data to see if there is..
- a relationship between variables
- a difference between groups

**hypothesis is an idea that can be tested

Question 6

describing a population with a population mean, it is a _____

Answer 6

parameter

Question 7

describing the sample with a sample mean, it is a ___

Answer 7

statistic

Question 8

important concepts for inferential statistics (7)

Answer 8

• sampling
• sample size
• confidence intervals
• probability
• statistical significance
• hypothesis testing
• level of significance

Question 9

what is population?

Answer 9

• the group of interest…the group that you want to generalize to
• you can never know a true pop value, only estimate in parameters. Do not report parameters

Question 10

what is sample?

Answer 10

• a subset of the population of interest
• used to study a health-state of interest and then use the information to make inferences about the larger population

-sample describe using sample statistics, estimate the pop parameters, when reporting results always report sample stats

Question 11

sampling method

Answer 11

• process researchers use to select subjects from the population being studied

*never assume that a random sampling method was used

Question 12

what are two types of samples?

Answer 12

• probability
• nonprobability

Question 13

probability sampling

Answer 13

= random sampling
- every member of the population has a chance of being selected
- the probability of being selected can be calculated

Question 14

what are probability sampling method? (3)

Answer 14

• simple random sampling
• stratified random sampling
• cluster sampling

Question 15

types of probability samples: simple random sample

Answer 15

• enumerate all members of population
• select the desired number of individuals at random
• each individual has same probability of being selected

Question 16

types of probability samples: stratified sample

Answer 16

• organize population into mutually exclusive strata
• select individuals at random within each stratum
• used when the population is naturally divided into subpopulation

Question 17

types of probability samples: cluster sample

Answer 17

• sample clusters or groups instead of individuals
  -examples might be schools, clinics, neighborhoods
• selection process is still random

Question 18

what are nonprobability sampling?

Answer 18

• the members of the population of interest do not have the same opportunity (equal chance) for selection into the study group (s)
• used when the researchers cannot use a random sampling method

Question 19

types of Nonprobability samples: convenience sample

Answer 19

-non probability sample (not for inference)
- literally a sample of convenience (individuals at the right place at the right time)

Question 20

types of Nonprobability samples: quota sample

Answer 20

• select a pre-determined number of individuals into sample from groups of interest
• participants are not randomly selected

Question 21

sampling error

Answer 21

• difference between the sample and the population
• always present in every sample
• researchers always attempt to minimize the sampling error

Question 22

sources of error in estimating parameters

Answer 22

-sample error:
- a sample may not accurately represent the population
- random selection from a population is the ideal nut there is still always some sampling error
- measurement error: accuracy of the measurements
- random error: noise

Question 23

sample size

Answer 23

• sample size is always important
• small sample size may not adequately represent the larger populations
• too small of a sample can lead to erroneous conclusions

**as the sample size increases, the error between the sample mean and the population mean should decrease

Question 24

what are two ways to make estimates?

Answer 24

1. could make your estimates using a single value
2. use a range of values

Question 25

point estimate

Answer 25

- a point estimate for a population parameter is the best single number estimate of that parameter - has the advantage of being very precise

Question 26

confidence interval

Answer 26

a range values for the estimated population parameter with a level of confidence attached -also known as interval estimates --> does not have the accuracy that point estimate has, but the interval estimate gives you more confidence

Question 27

statistical (null) hypothesis

Answer 27

- starts with the assumption that there is no difference or no association between the groups or variables

Question 28

what does it mean if p-value is ≥ .05?

Answer 28

that means that there is NOT a statistical relationship

Question 29

what does it mean if p-value is <.05?

Answer 29

that means that there is a statistical relationship

Question 30

type 1 error

Answer 30

- the p-value is statistically significant (<0.05) - the researcher rejects the null hypothesis and accepts the alternative (research) hypothesis this means that the null hypothesis was incorrectly rejected and the research hypothesis is incorrectly accepted which means that the researcher concluded that there was a difference between treatment groups or as association between variables when there was none

Question 31

type 2 error

Answer 31

- the p-value is not statistically significant (≥0.05) - the researcher does not reject the null hypothesis this means that the null hypothesis was not rejected which mean that the researcher incorrectly conclbuded that there was not a difference between treatment groups or an association between variables when there really was one **too small of a sample size can lead to type 2 error