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1. Estimation
Statistical
Methods
Descriptive
Statistics
Inferential
Statistics
Estimation
ESTIMATION
Hypothesis
Testing
Sampling Distribution
(a.k.a. “Distribution of Sample Outcomes”)
 Based
on the laws of probability
 “OUTCOMES” = proportions, means, etc.
 Infinite number of random samples  all
possible sample outcomes

And the probability of obtaining each one
 Allows us to estimate:
 What is the likelihood of obtaining our particular
sample outcome?
 Or, “There is a X% chance that the true population
parameter is within +/- some distance from this
sample outcome.
ESTIMATION
To estimate (make an “inference”)
population parameters from sample
statistics
 Why Necessary?
 Most commonly used for polling data

 Point
estimate
 Confidence intervals
Estimation1 : Pick
Confidence Level
 Confidence

LEVEL
Probability that the unknown population
parameter falls within the confidence interval
Confidence level = 1 - 
 Alpha () is the probability that the parameter is
NOT within the interval

Estimation 2: Find Appropriate Z-score

Divide the probability of error () equally into the
upper and lower tails of the distribution

For 95% confidence level, ( = .05) the area under curve
must equal 5%. The corresponding Z score for this is 1.96.
0.95
Sampling
Distribution
.025
.025
-1.96
 Z scores 
1.96
Estimation 3 : Constructing an
Interval Estimate



What is your point estimate?
How many “standard errors” do you want to go
out (on sampling distribution) from this point
estimate?
What is a particular standard error “worth” for
our sample outcome?
 Takes into account sample size (N) and
dispersion/heterogeneity

For proportions, the p (1-p) part of the equation
Constructing a Confidence Interval
for Proportions


What is your point estimate?  proportion
How many “standard errors” do you want to go out from
this point estimate?




1.65 Standard Errors  alpha of .10 (Confidence level of 90%)
1.96 Standard Errors  alpha of .05 (Confidence level of 95%)
2.58 Standard Errors  alpha of .01 (Confidence level of 99%)
What is a particular standard error “worth” for out sample
outcome?  Everything after the “z” in formula 7.3 in
Healey book
Numerator = (your proportion) (1- proportion)
 Generic = .50  .25 in numerator

Example:

UMD Poll Finds that Dr. Maahs is incapable instructor.



1.
2.
3.

Random poll of 340 UMD students
60% agree with the statement, “Professor Maahs is an incompetent boob.”
Get into groups of 3 or less and calculate confidence intervals for
the following:
Alpha () = .05
Alpha () = .05, Change N to 700

What happens to confidence interval and why does this
make sense?
Alpha () = .05, N = 700, Change proportion to from .60 to .96

What happens to confidence interval and why does this
make sense?
FOR EACH, WRITE OUT IN WORDS how you would express these
confidence intervals. “There is a 95% chance...”
Estimation of Population Means

EXAMPLE:
A researcher has gathered information from a
random sample of 178 households in Duluth.
Construct a confidence interval to estimate the
population mean at the 95% level:

An average of 2.3 people reside in each household.
Standard deviation is .35.
Constructing a Confidence
Interval for Means


What is your point estimate?  mean
How many “standard errors” do you want to go out from
this point estimate?



1.96 Standard Errors  alpha of .05
2.58 Standard Errors  alpha of .01
What is a particular standard error “worth” for out sample
outcome?  s/√N-1

We don’t know the population standard deviation (σ) so we substitute our best
guess

BUT, we subtract one to “correct” for bias
Application to Example


What is your point estimate?  2.3 people per household
How many “standard errors” do you want to go out from
this point estimate?



1.96 Standard Errors  alpha of .05
What is a particular standard error “worth” for out sample
outcome?  .35 /√178-1
Formula


c.i.95% = z +/- (s/√N-1) = 1.96 (.35 /√178-1) = .0515
95% sure that over the long run, the average number of people in Duluth
households (THE WHOLE POPULATION) is between 2.25 and 2.35.
In groups, construct confidence intervals for
the following means

A random sample of 429 college students was interviewed



They reported they had spent an average of $178 on
textbooks during the previous semester. If the standard
deviation (s) of these data is $15 construct an estimate of
the population at the 95% confidence level.
They reported they had missed 2.8 days of class per
semester because of illness. If the sample standard
deviation is 1.0, construct an estimate of the population
mean at the 99% confidence level.
Two individuals are running for mayor of Duluth. You
conduct an election survey of 100 adult Duluth residents 1
week before the election and find that 45% of the sample
support candidate Long Duck Dong, while 40% plan to vote
for candidate Singalingdon.

Using a 95% confidence level, based on your findings, can you
predict a winner?
Review: What influences confidence
intervals?

The width of a confidence interval depends on
three things

: The confidence level can be raised (e.g., to
99%) or lowered (e.g., to 90%)

N: We have more confidence in larger sample sizes
so as N increases, the interval decreases

Variation: more variation = more error


For proportions, % agree closer to 50%
For means, higher standard deviations
Hypothesis Testing (intro)
Statistical
Methods
Descriptive
Statistics
Inferential
Statistics
Estimation
Estimation
HYPOTHESIS
Hypothesis
TESTING
Testing
Hypothesis Testing
 Hypothesis
(Causal)
A
prediction about the relationship between 2 variables that
asserts that changes in the measure of an independent
variable will correspond to changes in the measure of a
dependent variable
 Hypothesis
 Is
testing
the hypothesis supported by facts (empirical data)
Hypothesis Testing & Statistical Inference

We almost always test hypotheses using sample
data
 Also
referred to as “significance testing”
 Draw
conclusions about the population based on sample
statistics

 Is
As a result, have to account for sampling error when testing
hypotheses
there a “statistically significant” finding
Research vs. Null hypotheses

Research hypothesis
 H1
 Typically

predicts relationships or “differences”
Null hypothesis
 Ho
 Predicts
“no relationship” or “no difference”
 Can usually create by inserting “not” into a correctly
worded research hypothesis

In Science, we test the null hypothesis!
DIRECTIONAL VS. NONDIRECTIONAL HYPOTHESES


Non-directional research hypothesis
 “There was an effect”
 “There is a difference”
Directional research hypothesis
 Specifies the direction of the difference (greater or
smaller) from the Ho
GROUP WORK
Testing a hypothesis 101
•
•
State the null & research hypotheses
Set the criteria for a decision
•
•
•
Alpha, critical regions for particular test statistic
Compute a “test statistic”
Make a decision
•
•
REJECT OR FAIL TO REJECT the null hypothesis
We cannot “prove” the null hypothesis (always some
non-zero chance we are incorrect)