CH03 - CHAPTER 3 Collecting Data Descriptive statistics...

Info iconThis preview shows pages 1–4. Sign up to view the full content.

View Full Document Right Arrow Icon
CHAPTER 3 Collecting Data Descriptive statistics – numerical and graphical summaries that characterize a dataset (Ch 2) Inferential statistics – sample data used to make conclusions about a broader range of individuals than just those who are observed For example, if I want to estimate the average height of female college students in the US, then I need to select a sample that is representative of this population. Bias – What Can Go Wrong? Selection bias – the sample is selected in a way that does not include the whole population Nonresponse bias – those selected to participate do not respond The Fundamental Rule for Using Data for Inference: Data can be used to make inference about a much larger group if the sample can be considered to be representative with regard to the question of interest
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Response bias – participants provide incorrect information Accuracy Margin of error – a measurement of accuracy Conservative Margin of Error = (1 / √n ) * 100% Confidence Interval – an interval of values that estimates an unknown population value Approximate 95% CI = sample proportion ± 1 / √n Choosing a Sample Size 1. Determine the margin of error desired 2. Solve for n in MoE = 1 / √n Methods of Selecting a Sample Simple random sample – every unit of the population has the same chance of being selected for the sample
Background image of page 2
and then randomly select 1000 names. Stratified Sampling – population divided into groups and then a simple random sample is taken from each group For example, I could divide the PSU student population into freshmen, sophomores, juniors, seniors, and graduates, and then take a random sample of 500 from each group. Cluster sampling – population divided into groups and a random sample of groups is selected, use all the individuals in those chosen groups For example, I could divide the PSU student population into dorm- dwellers, on-campus apartment dwellers, off-campus apartment dwellers, off-campus house dwellers, fraternity dwellers, and others. Then, I take a random sample of 2 groups and use their responses. Systematic sampling – population list provided and every k th individual in the entire population For example, I could generate a list of all enrolled students at PSU and sample every 100 th name. What Else Can Go Wrong?
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
Image of page 4
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 11

CH03 - CHAPTER 3 Collecting Data Descriptive statistics...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online