We’ve been limited to date being given to us. But we can collect it ourselves using specific sampling techniques. Chapter 12: Sample Surveys

Idea 1: Part of the Whole – Collect data on everyone in the population (census) if possible – If not, you must sample: examine a smaller group that represents the population – Ex: Opinion Polls With Surveys: Ask specific questions. careful phrasing questions.

Bias methods that over- or under- emphasize parts of the population are biased. Undercoverage part of the population is not sampled at all or has a smaller representation – Identify the bias, if any. – Identify what it causes. – Avoid bias by sampling at random.

Types of Bias Voluntary Response Bias: people invited to respond and only those who respond are counted Ex: lunch time poll about school food Nonresponse Bias: if the opinion of who don’t respond might be different from those who do respond (leaving people out) Ex: telephone polls Response Bias: influencing the responses in any way. If misleading or personal questions. Ex: “Some say if you haven’t been kissed by 16, you’re a loser. At what age was your 1 st kiss?”

Idea 2: Randomize Randomizing protects us from lurking variables. – on average the sample reflects the population – Able to make valid conclusions

Idea 3: Sample Size The sample size/randomness itself is what matters, not the size of the population Unless the population is too small and you’re using more than 10% of it in the sample, then it matters

Notation We use sample statistics to estimate the population parameters.

1. The sampling frame is a list of individuals from which the sample is drawn. 2. assign a random number to each individual.

– If we sample over and over again, will all the results be the same? – No. – We call these sample-to-sample differences sampling variability.

Simple Random Samples Each individual has an equal chance of being selected. Each combination of people has an equal chance of being selected. A sample drawn in this way is called a Simple Random Sample (SRS).

Stratified Sampling Slice the population into similar groups, called strata, before the sample is selected. Then take a sample from each group. **Stratify when you have different groups that may have different results ***it reduces variability.

Stratified can reduce bias. Stratified helps us notice important differences among groups.

Cluster Sampling Splitting the population into similar parts or clusters. – Then we could select one or a few clusters at random – Perform a census within each cluster – Clusters are made up of mixed groups – Not the same as stratifying, because a strata is made up of one group

Systematic Samples – Ex: every 10th person on an ordered list. Select the 1 st individual randomly. Order your sampling frame and number each person Then decided to select every kth person for your sample

Combining several sampling methods is multistage sampling. convenience sampling, we simply include the individuals who are convenient. Example: A business sampling their own customers.

Chapter 12: Assignment Pg. 288: #1, 3, (15-27)odd, (31-35)odd