Hypothesis Testing with z Tests Chapter 7 Slide 2 The z Table
>Benefits of standardization: allowing fair comparisons >z
table: provides percentage of scores between the mean and a given z
score Slide 3 Raw Scores, z Scores, and Percentages >Step 1:
Convert raw score to z score >Step 2: Look up area in Table The
table presents area between the Mean and z and beyond the mean and
z. Slide 4 From Percentages to z Scores >Step 1: Use the z table
in reverse, taking a percentage and converting it into a z score.
>Step 2: Convert the z score to a raw score using the formula.
Slide 5 Sketching the Normal Curve >The benefits of sketching
the normal curve: Stays clear in memory; minimizes errors Practical
reference Condenses the information Slide 6 Slide 7 The
Standardized z Distribution Slide 8 Calculating the Percentile for
a Positive z Score Slide 9 Calculating the Percentage Above a
Positive z Score Slide 10 Calculating the Percentage at Least as
Extreme as Our z Score Slide 11 Calculating the Percentile for a
Negative z Score Slide 12 Calculating the Percentage Above a
Negative z Score Slide 13 Calculating the Percentage at Least as
Extreme as Our z Score Slide 14 Calculating a Score from a
Percentile Slide 15 Check Your Learning >If the population mean
is 10 and the standard deviation is 2: What is the percentile rank
of a sample mean of 6? of 11? What percentage of the samples would
score higher than a score of 6? of 11? Slide 16 The Assumptions and
the Steps of Hypothesis Testing >Requirements to conduct
analyses Assumption: characteristic about a population that we are
sampling necessary for accurate inferences Slide 17 Parametric v.
Nonparametric Tests >Parametric tests: inferential statistical
test based on assumptions about a population >Nonparametric
tests: inferential statistical test not based on assumptions about
the population Slide 18 Slide 19 Slide 20 An Example of the z Test
>The z test When we know the population mean and the standard
deviation >The z test The six steps of hypothesis testing >H
0, H 1 >One-tailed vs. two-tailed tests Slide 21 Determining
Critical Values for a z Distribution One tailed or two- tailed test
for significance? Slide 22 Making a Decision Slide 23 Check Your
Learning >IQ scores are designed to have a mean of 100 and a
standard deviation of 15. Assume the class mean is 114. Go through
the six steps of hypothesis testing. Slide 24 Dirty Data >Dirty
Data: Missing data, misleading data, and outliers >Misleading
data: The famous butterfly ballot used in Florida during the 2000
presidential election showed the of the arrangement of items on a
page. Slide 25 Cleaning Dirty Data >Judgment calls need to be
made. >The best solution is to report everything so that other
researchers can assess the trade-offs. >The best way to address
the problem of dirty data is replication.
