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## 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 numerical ... – PowerPoint PPT presentation

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Title: Descriptive & Inferential Statistics

1
Descriptive Inferential Statistics
• Merryellen Towey Schulz, Ph.D.
• College of Saint Mary
• EDU 496

2
The Meaning of Statistics Several Meanings
• Last years enrollment figures
• Average enrollment per month last year
• Evaluators made a projection of next years
enrollments
• Collections of numerical data
• Summary measures calculated from a collection of
data
• Activity of using and interpreting a collection
of numerical data

3
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

4
Descriptive Statistics Associated with Methods
and Designs
5
Descriptive Stats Vocabulary
• Central tendency
• Mode
• Median
• Mean
• Variation
• Range
• Standard deviation
• Normal distribution

6
Descriptive Stats Vocabulary contd
• Standard score
• Effect size
• Correlation
• Regression

7
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

8
Inferential Stats Vocabulary
• Hypothesis
• Null hypothesis
• Alternative hypothesis
• ANOVA
• Level of significance
• Type I error
• Type II error

9
Examples of Descriptive and Inferential Statistics
• Descriptive Statistics
• Graphical
• Arrange data in tables
• Bar graphs and pie charts
• Numerical
• Percentages
• Averages
• Range
• Relationships
• Correlation coefficient
• Regression analysis
• Inferential Statistics
• Confidence interval
• Margin of error
• Compare means of two samples
• Pre/post scores
• t Test
• Compare means from three samples
• Pre/post and follow-up
• ANOVA analysis of variance

10
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?

11
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

12
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