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Statistical Sciences

Statistical Sciences 1023A/B

Jennifer Waugh

Fall

Description

Stats 1023 Midterm exam
Textbook notes
Chapter 1
To conduct a statistical study properly, one must:
1. Get a representative sample
2. Get a large enough sample
3. Decide whether the study should be an observational study or randomized experiment
Chapter 2
Data: information collected through numbers or other pieces which has meaning attached
The two most common sources for statistical studies are academic conferences and scholarly journals
Academic conferences: held annually, in which researchers share information with others. Used to catch
up on news stories
- Downfall: there is unlikely to be a corresponding written report by researchers, so it is
diffilcult for researchers to obtain further information
Scholarly journal: reporters routinely read these journals when they are published, and get press
releases.
Seven Critical Components
1. The source of the research and its funding
2. The researchers who had contact with the participants
3. The individuals studied and how they were selected
4. The exact nature of the measurements made and questions asked
5. The setting in which the measurements were taken
6. Differences in the groups being compared, in addition to the factor of interest
7. The extent or size of any claimed effects or differences
Chapter 3
Pitfalls encountered when asking questions in a survey:
1. Deliberate bias
Phrasing questions to obtain a certain answer
2. Unintentional bias
Questions worded where the meaning is misinterpreted 3. Desire to please
Desire to please the person asking the question. Understate responses about undesirable social habits
4. Asking the uninformed
People do not like to admit that they don’t know what you are talking about when you ask them a
question
5. Unnecessary complexity
Questions need to be simple
6. Ordering of questions
Requiring respondents to think about something that they may have not considered, and then order
another question to obtain those results
7. Confidentiality of anonymity
Sometimes people answer questions based on the degree to which they think they are anonymus
Open Question: answer in their own words
Closed Questions: list of answers to choose from
Categorical variables: are those we can place into a category but may not have any logical ordering.
(male and female)
Ordinal variable: categories that have natural order. (strongly agree – strongly disagree)
Nominal variable: do not have natural order
Measurement variables (quantitative variables): are those which we can record a numerical value and
then order respondents accordingly. (IQ)
Interval variable: a measurement where it makes sense to talk about differences, but not as
ratios. (weather. 20 degrees last night, 40 degrees today)
Ratio variable: has a meaningful measure of 0, and makes sense to talk about as a ratio. (pulse
rate at 60 before you start, and 120 after. You doubled it)
Validity: a measurement is one that actually measures what it claims to measure
Reliability: something that will give you the same result time after time
Bias measurement: prejudice in one direction
Measurement error: amount by which each measurement differs from the true value Natural variability: natural changes across time in the system (unemployment rate)
Chapter 4
Randomized experiments
Experiment: measures the effect of manipulating the environment in some way
Randomized experiment: manipulation is assigned in a random basis
Explanatory variable: measure the result of the feature being manipulated (independent variable)
Response variable: the outcome of a manipulation (dependent variable)
Observational study: resembles an experiment except that the manipulation occurs naturally and is
observed
Meta Analysis: a quantitative review of a collection of studies all done on a similar topic (a bunch of
studies on the same thing- mammogram results)
Case study: an indepth examination of a small number of individuals
Margin of error: the measure of accuracy in numbers. The sample proportion differs from the
population proportion by more than the margin of error less than 5% of the time, or 1 in 20 surveys.
Advantages of sample surveys:
- When a census isn’t possible (blood concentration)
- Speed (amount of times it takes to conduct)
- Accuracy (devote your resources to most accurate info possible from the sample selected)
Simple random sampling: the ability of a relatively small sample to accurately reflect the population
Probability sampling plans: everyone in the population must have a specified chance of making It into
the sample
Simple random sampling: every conceivable group of people of the required size has the same chance of
being selected
Need two thing:
- Random numbers: a source of random numbers generated to help calculate
- A list of units in the population
Stratified random sampling: collected by dividing the population into units groups called strata and
taking a random sample from each. (example political parties) Cluster sampling: population is divide into clusters,

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