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Creating User Interfaces

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Basic technique when it is impossible or too expensive to measure everything ... Admire Michelle Obama example. Belief in Holocaust example. Usability research ... – PowerPoint PPT presentation

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Title: Creating User Interfaces


1
Creating User Interfaces
  • Review midtermSampling
  • Homework User observation reports due next week

2
Sampling
  • Basic technique when it is impossible or too
    expensive to measure everything/everybody
  • Premise possible to get random sample, meaning
    every member of whole population equally likely
    to be in sample
  • NOTE not a substitute for monitoring directly
    activity on / with interface

3
Source
  • The Cartoon guide to Statistics by Larry Gonick
    and Woollcott SmithHarperResource
  • Procedures (formulas) presented without proof,
    though, hopefully, motivated

4
Task
  • Want to know the percentage (proportion) of some
    large group
  • adults in USA
  • television viewers
  • web users
  • For a particular thing
  • think the president is doing a good job
  • watched specific program
  • viewed specific commercial
  • visited specific website

5
Strategy Sampling
  • Ask a small group
  • phone
  • solicitation at a mall
  • Follow-up or prelude to access to webpage
  • other?
  • Monitor actions of a small group, group defined
    for this purpose
  • Monitor actions of a panel chosen ahead of time
  • ALL THESE make assumption that those in group
    are similar to the whole population.

6
Two approaches
  • Estimating with confidence intervalc in general
    population based on proportionphatin sample
  • Hypothesis testingH0 (null hypothesis) p p0
    versusHa p p0

7
Estimation process
  • Construct a sample of size n and determine phat
  • Ask who they are voting for (for now, let this
    be binomial choice)
  • Use this as estimate for actual proportion p.
  • but the estimate has a margin of error. This
    means The actual value is within a range
    centered at phat UNLESS the sample was really
    strange.
  • The confidence value specifies what the chances
    are of the sample being that strange.

8
Statement
  • I'm 95 sure that the actual proportion is in the
    following range.
  • phat m
  • Notice if you want to claim more confidence, you
    need to make the margin bigger.

9
Image from Cartoon book
  • You are standing behind a target.
  • An arrow is shot at the target, at a specific
    point in the target. The arrow comes through to
    your side.
  • You draw a circle (more complex than/- error)
    and sayChances arethe target point is inthis
    circle unless shooterwas 'way off' . Shooter
    would only be way off X percent of the
    time.(Typically X is 5 or 1.)

10
Mathematical basis
  • Samples are themselves normally distributed
  • if sample and p satisfy certain conditions.
  • Most samples produce phat values that are close
    to the p value of the whole population.
  • Only a small number of samples produce values
    that are way off.
  • Think of outliers of normal distribution

11
Actual (mathematical) process
Sample size must be this big
  • Can use these techniques when np5 and
    n(1-p)5
  • The phat values are distributed close to normal
    distribution with standard deviation sd(p)
  • Can estimate this using phat in place of p in
    formula!
  • Choose the level of confidence you want (again,
    typically 5 or 1). For 5 (95 confident),
    look up (or learn by heart the value 1.96 this
    is the amount of standard deviations such that
    95 of values fall in this area. So .95 is
    P(-1.96

12
Notes
  • p is less than 1 so (1-p) is positive.
  • Margin of error decreases as p varies from .5 in
    either direction. (Check using excel).
  • if sample produces a very high (close to 1) or
    very low value (close to 0), p (1-p) gets
    smaller
  • (.9)(.1) .09 (.8)(.2) .16, (.6)(.4) .24
    (.5).5).25

13
Notes
  • Need to quadruple the n to halve the margin of
    error.

14
Formula
  • Use a value called the z transform
  • 95 confidence, the value is 1.96

15
(No Transcript)
16
Mechanics
  • Process is
  • Gather data (get phat and n)
  • choose confidence level
  • Using table, calculate margin of error.
  • Book example 55 (.55 of sample of 1000) said
    they backed the politician)
  • sd(phat) square_root ((.55)(.45)/1000)
    .0157
  • Multiply by z-score (e.g., 1.96 for a 95
    confidence) to get margin of error
  • So p is within the range .550 (1.96)(.0157)
    and .550 (1.96)(.0157)
  • .519 to .581 or 51.9 to 58.1

17
Example, continued
  • 51.9 to 58.1
  • may round to 52 to 58
  • or
  • may say 55 plus or minus 3 percent.
  • What is typically left out is that there is a
    1/20 chance that the actual value is NOT in this
    range.

18
95 confident means
  • 95/100 probability that this is true
  • 5/100 chance that this is not true
  • 5/100 is the same as 1/20 so,
  • There is only a 1/20 chance that this is not
    true.
  • Only 1/20 truly random samples would give an
    answer that deviated more from the real
  • ASSUMING NO INTRINSIC QUALITY PROBLEMS
  • ASSUMING IT IS RANDOMLY CHOSEN

19
99 confidence means
  • Give fraction positive
  • Give fraction negative

20
Why
  • Confidence intervals given mainly for 95 and
    99??
  • History, tradition, doing others required more
    computing.

21
Let's ask a question
  • How many of you watched the last Super Bowl?
  • Sample is whole class
  • How many registered to vote?
  • Sample size is number in class 18 and older
  • ????

22
Excel columns A B
23
Variation of book problem
Divisor smaller
  • Say sample was 300 (not 1000).
  • sd(phat) square_root ((.55)(.45)/300)
    .0287
  • Bigger number. The circle around the arrow is
    larger. The margin is larger because it was
    based on a smaller sample. Multiplying by 1.96
    get .056, subtracting and adding from the .55 get
  • .494 to .606You/we are 95 sure that true
    value is in this range.
  • Oops may be better, but may be worse. The fact
    that the lower end is below .5 is significant for
    an election!

24
Exercise
  • size of sample is n
  • proportion in sample is phat
  • confidence level produces factor called the
    z-score
  • Can be anything but common values are 80, 90,
    95, 99)
  • Use table. For example, 95 value is 1.96 99
    is 2.58
  • Calculate margin of error m
  • m zscore sqrt((phat)(1-phat)/n)
  • Actual value is phat m and

25
Opportunity sample
  • Common situation
  • people assigned/asked to have a meter attached to
    their TVs
  • people asked/voluntarily sign up to have a meter
    (software) installed in their computers.
  • people asked during a Web session to participate
    in survey
  • students in a specific class!
  • Practice is to determine categories
    (demographics) and project the sample results to
    the subpopulation to the population
  • For example, if actual population was 52 female
    and 48 male, and sample (panel) is 60 male and
    40 female, use proportions to adjust result
  • But maybe this fact hides problem with the sample
  • Has negative features of any opportunity sample
  • Are these folks different than others in their
    (sub)population?

26
Requirements
  • Model / Categories must be well-defined and valid
  • Hispanic versus (Cuban, others) in Florida in
    2000
  • Need independent analysis of subpopulations
    representation in general population
  • The sample sizes are the individual Ns, making
    the margin of errors larger

27
Adjustment from panel data
  • Panel of 10 6 females, 4 males
  • Population is 52 female and 48 male
  • Female panelists 5 liked interface, 1 didn't.
    Male panelists 2 liked interface, 2 didn't.
  • Estimate for whole population (size P)
  • (5/6) .52 P (2/4).48 P

28
Critical part of surveys
  • and survey analysis
  • Understand the exact wording of question.
  • Understand definition of categories of
    population.
  • Don't make assumptions
  • Admire Michelle Obama example
  • Belief in Holocaust example

29
Usability research
  • Often aims for qualitative, not quantitative
    results
  • Ideas, critical factors
  • Note there are fields of study
  • Non-numeric statistics
  • Qualitative research
  • Still necessary to be systematic.
  • AD consider taking Statistics!

30
Homework
  • Continue work on user observation studies
  • This is qualitative work
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