# Descriptive & Inferential Statistics

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```					Descriptive & Inferential
Statistics
Merryellen Towey Schulz, Ph.D.
College of Saint Mary
EDU 496
The Meaning of Statistics
Several Meanings
   Collections of             Last year’s
numerical data              enrollment figures
   Summary measures           Average enrollment
calculated from a           per month last year
collection of data
   Activity of using and      Evaluators made a
interpreting a              projection of next
collection of               year’s enrollments
numerical data
Descriptive Statistics
 Use of numerical information to
summarize, simplify, and present data.
 Organized and summarized for clear
presentation
 For ease of communications
 Data may come from studies of
populations or samples
Descriptive Statistics Associated
with Methods and Designs
Design                       Descriptive Statistics
Survey Studies               Percentages, measures of
central tendency and variation
Meta-analysis                Effect sizes
Causal comparative studies   Measures of central tendency &
variation, percentages, standard
scores
Experimental                 Measures of central tendency &
variation, percentages, standard
scores, effect sizes
Descriptive Stats Vocabulary
 Central tendency
 Mode
 Median
 Mean
 Variation
 Range
 Standard deviation
 Normal distribution
Descriptive Stats Vocabulary
cont’d
 Standard score
 Effect size
 Correlation
 Regression
Inferential Statistics

 To generalize or predict how a large
group will behave based upon
information taken from a part of the
group is called and INFERENCE
 Techniques which tell us how much
confidence we can have when we
GENERALIZE from a sample to a
population
Inferential Stats Vocabulary
 Hypothesis
 Null hypothesis
 Alternative hypothesis
 ANOVA
 Level of significance
 Type I error
 Type II error
Examples of Descriptive and
Inferential Statistics
Descriptive Statistics             Inferential Statistics
   Graphical                         Confidence interval
–   Arrange data in tables       Margin of error
–   Bar graphs and pie
   Compare means of two
charts
samples
   Numerical                          –   Pre/post scores
–   Percentages
–   t Test
–   Averages
   Compare means from
–   Range
three samples
   Relationships                      –   Pre/post and follow-up
–   Correlation coefficient
–   ANOVA = analysis of
–   Regression analysis               variance
Problems With Samples
   Sampling Error
–   Inherent variation between sample and population
–   Source is “chance or luck”
–   Results in bias
   Sample statistic -- a number or figure
–   Single measure -- how sure accurate
–   Comparing measures --see differences
 How much due to chance?
 How much due to intervention?
What Is Meant By A Meaningful
Statistic (Significant)?
   Statistics, descriptive or inferential are NOT a
substitute for good judgment
–   Decide what level or value of a statistic is
meaningful
–   State judgment before gathering and analyzing
data
   Examples:
–   Score on performance test of 80% is passing
–   Pre/post rules instruction reduces incidents by
50%
Interpretation of Meaning
   Population Measure (statistic)
–   There is no sampling error
–   The number you have is “real”
–   Judge against pre-set standard
   Inferential Measure (statistic)
–   Tells you how sure (confident) you can be
the number you have is real
–   Judge against pre-set standard and state
how certain the measure is

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 views: 238 posted: 3/14/2012 language: English pages: 12