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\title{CS6963 Lecture \#3}
\author{Robert Ricci}
\date{January 14, 2014}

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\1 Left over from last time
    \2 Who still doesn't have the textbook?
    \2 To post assignments on canvas or not?

\1 Today is all about mistakes

\1 Mistakes from the book:
    \2 No goals: identify problem (metrics, workloads, and methodology)
    \2 Biased goals: prove my system better than theirs
    \2 Unsystematic approach: If you miss things, your conclusion may be wrong
    \2 Analysis without understanding the problem: ``A problem well stated is
        half solved'. Especially a problem when applying solutions to areas
        outside expertise
    \2 Incorrect performance metrics: Metrics that solve the problem, comparing
        apples to apples (eg. MIPS for CISC vs. RISC) Often chose easy rather
        than relevant
    \2 Unrepresentative workload: Representative of actual usage
    \2 Wrong technique: Using the hammer that you have. Are you capturing all
        important parts of the environment?
    \2 Overlooking important parameters: Choice of parameters matters a lot
    \2 Ignoring significant factors: factors are the parameters you vary.
        Scientific method; control vs. variables. Sensitivity analysis:
        something we rarely do!
    \2 Inappropriate experiment design: how many experiments, how many 
        trials, parameter choice
    \2 Inappropriate levels of detail: ``Forest for the trees''
    \2 No analysis: have to tell the reader what they see
    \2 Erroneous analysis: base analysis on bogus techniques such as 
        average of ratios or bad experiments
    \2 No sensitivity analysis: Analysis gives you evidence, not facts.
        What would change if setting changes?
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    \2 Ignoring errors in input: errors may be systematically biased
        (eg. queues too long, drop work)
    \2 Improper treatment of outliers: Is it an artifact of the evaluation
        environment, or can it really happen?
    \2 Assuming no change in the future: Big one for research, which is
        supposed to be forward looking---assumptions and sensitivity analysis
    \2 Ignoring variability: Don't just present mean! - eg. bimodal high/low
        distribution
    \2 Too complex analysis: Don't make it complex just for the sake of seeming
        hard
    \2 Improper presentation of results: Remember, about persuasion
    \2 Ignoring social aspects: Again, about persuasion. Remember: you know 
        a lot that the audience doesn't. Audience is rarely as expert as you in
        the details of the thing you're writing about. Beliefs, values,
        language, jargon.
    \2 Omitting assumptions and limitations: Important in research---under what
        conditions do you expect your conclusions to be true?
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\1 For next time
    \2 Read 2.2
    \2 HW \#2 due before class starts
    \2 HW \#3 will be handed out by class time
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    \3 Need three volunteers to go first for paper anaylsis
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