Lesson_01_Notes - Gathering Data...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
Introduction Let's get started! Here is what you will learn in this lesson. Learning objectives for this lesson Upon completion of this lesson, you should be able to do the following: Recognize and distinguish between various sampling schemes Understand the importance of random sampling as it applies to the study of statistics Designing Samples Then entire group of individuals about which information is wanted is called the populations . It ma be somewhat abstract. The part of the population actually examined to gather information is the sample . It is more concrete and immediate than the population. Example Identify the population and the sample in the following: A survey is carried out at a university to estimate the proportion of undergraduates living at home during the current term. Population : all undergraduates at the university; Sample : those undergraduates surveyed. 1. In 2005, investigators chose 400 teachers at random from the National Science Teachers Association list and polled them as to whether or not they believed in biblical creation (hypothetical scenario). 200 hundred of the teachers sampled responded. Population : National Science Teachers Association members; Sample : the 200 respondents. 2. A balanced coin is flipped 500 times and the number of heads is recorded. Population : all coin flips; Sample : the 500 coin flips. 3. Any sample taken should be selected at random; otherwise it may be prone to bias. Bias is the systematic favoring of certain outcomes. For example, people who volunteer for a treatment may bias results toward conclusions that the treatment offers an improvement over a current treatment. A sample should be selected using some probability sampling design which gives each individual or participant a chance of being selected. Four common probability sampling schemes are: Simple Random Sampling (SRS) – a simple random sample of size N consists of N individuals from the population chosen in such a way that every set of N individuals has an equal chance of being selected. 1. Stratified Random Sampling – The population is divided into important subgroups (e.g. East and West; Freshmen, Sophomore, Junior, Senior) which are groups of individuals or subjects that are similar in a way that may affect their response – think of stratifying a university’s undergraduate population by race, gender, or nationality. Then separate simple random samples are taken from each subgroup. These subgroups are called strata. This is done to be sure every important subgroup is represented properly in the overall sample which will enhance the efficiency of this design. 2.
Background image of page 1

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

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 10/08/2010 for the course STAT 200 taught by Professor Barroso,joaor during the Fall '08 term at Penn State.

Page1 / 4

Lesson_01_Notes - Gathering Data...

This preview shows document pages 1 - 2. Sign up to view the full document.

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