SlideShare a Scribd company logo

1a difference between inferential and descriptive statistics (explanation)

Download as pptx, pdf
Ken Plummer
Ken Plummer

The document discusses descriptive and inferential statistics. Descriptive statistics describe the features of a data set using numerical measures like the range, mode, and mean. Inferential statistics draw conclusions about a larger population based on analyzing a sample, allowing inferences to be made about the population. The example shows a teacher using descriptive statistics to answer a parent's questions about their child's spelling test scores and the class data. The parent then asks inferential questions comparing the class to other groups, allowing the teacher to infer how the sample class compares more broadly.

1 of 58
Downloaded 640 times
Determining whether to use Inferential and Descriptive Statistics Learning Module: Explanation
Welcome to this learning module!
In this module you will learn the difference between
Inferential and Descriptive statistics!
In later modules you will then determine when a question should be answered with one or the other.
First, Descriptive statistics!
Simply put, descriptive statistics describe the features of a data set using numerical measures.
Simply put, descriptive statistics describe the features of a data set using numerical measures. And that’s it!
Simply put, descriptive statistics describe the features of a data set using numerical measures. And that’s it! Here’s an example
Meet Mrs. Graham,
a fifth Grade teacher having a parent teacher conference.
The parent has some questions about her son Bobby.
She will base her answers off of a class data set.
She will base her answers off of a class data set. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
She will base her answers off of a class data set. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data
She will base her answers off of a class data set. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
Here are the parent’s questions with Mrs. Graham’s answers Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What was Bobby’s score?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 32
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Out of how many possible?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 50
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What were the highest and lowest scores?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Highest 50, Lowest 31
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What was the class average?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 About 42
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What was the most common score?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 45
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Look’s like Bobby needs more study time!
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 That’s probably a good idea.
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
Back to our definition: Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
descriptive statistics describe the features of a data set using numerical measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
descriptive statistics describe the features of a data set using numerical measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
descriptive statistics describe the features of a data set using numerical measures Numerical Measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
descriptive statistics describe the features of a data set using numerical measures Numerical Measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Highest to Lowest Score – THE RANGE Bella 43 Most Common Score – THE MODE Betty 45 Average Score – THE MEAN Bobby 32 Etc. Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
Now for Inferential statistics! • While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
• While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
• While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions or INFER about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
• While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions or INFER about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
• While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions or INFER about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • INFERential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
Same example, but here come a series of inferential questions from the parent.
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 How does your class compare with other 5th grade classes: In the school?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 About the same
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What about other 5th grade classes: In the district?
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 A little better
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What about other 5th grade classes: In the State
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 We are higher
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What about other 5th grade classes: In the Country
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 We are much higher
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Looks like your class Maybe does not Bobby represent doesn’t the national have to population study as and is much? probably doing better.
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Maybe.
Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
In summary - A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population. Note – inferential and descriptive studies use the SAME statistics (e.g., mean, range, mode, etc.), but as you can see – for DIFFERENT reasons!
Ad

Recommended

RESEARCH METHODOLOGY- PROCESSING OF DATA
RESEARCH METHODOLOGY- PROCESSING OF DATARESEARCH METHODOLOGY- PROCESSING OF DATA
RESEARCH METHODOLOGY- PROCESSING OF DATA
jeni jerry
 
Data analysis
Data analysisData analysis
Data analysis
Aleeza Ahmad
 
Parametric test - t Test, ANOVA, ANCOVA, MANOVA
Parametric test - t Test, ANOVA, ANCOVA, MANOVAParametric test - t Test, ANOVA, ANCOVA, MANOVA
Parametric test - t Test, ANOVA, ANCOVA, MANOVA
Princy Francis M
 
FREQUENCY DISTRIBUTION.pptx
FREQUENCY DISTRIBUTION.pptxFREQUENCY DISTRIBUTION.pptx
FREQUENCY DISTRIBUTION.pptx
SreeLatha98
 
Standard deviation
Standard deviationStandard deviation
Standard deviation
P. G. Department of Botany, Berhampur University
 
Multivariate Analysis
Multivariate AnalysisMultivariate Analysis
Multivariate Analysis
Stig-Arne Kristoffersen
 
Parametric and nonparametric test
Parametric and nonparametric testParametric and nonparametric test
Parametric and nonparametric test
ponnienselvi
 
Type I and Type II Errors in Research Methodology
Type I and Type II Errors in Research MethodologyType I and Type II Errors in Research Methodology
Type I and Type II Errors in Research Methodology
Dr. Chinchu C
 
Presentation of Data (thesis writing)
Presentation of Data (thesis writing)Presentation of Data (thesis writing)
Presentation of Data (thesis writing)
Charlene Eve Saligumba
 
Wilcoxon signed rank test
Wilcoxon signed rank testWilcoxon signed rank test
Wilcoxon signed rank test
Biswash Sapkota
 
Types of Variables
Types of VariablesTypes of Variables
Types of Variables
Ali Mustafa
 
wilcoxon signed rank test
wilcoxon signed rank testwilcoxon signed rank test
wilcoxon signed rank test
raj shekar
 
Confirmatory Factor Analysis
Confirmatory Factor AnalysisConfirmatory Factor Analysis
Confirmatory Factor Analysis
Economic Research Forum
 
Analysis of variance (anova)
Analysis of variance (anova)Analysis of variance (anova)
Analysis of variance (anova)
Sadhana Singh
 
Point biserial correlation example
Point biserial correlation examplePoint biserial correlation example
Point biserial correlation example
Muhammad Khalil
 
Threats to internal and external validity
Threats to internal and external validityThreats to internal and external validity
Threats to internal and external validity
rodsazon
 
Experimental Design | Statistics
Experimental Design | StatisticsExperimental Design | Statistics
Experimental Design | Statistics
Transweb Global Inc
 
Parametric vs Non-Parametric
Parametric vs Non-ParametricParametric vs Non-Parametric
Parametric vs Non-Parametric
Aniruddha Deshmukh
 
Statistical tests
Statistical tests Statistical tests
Statistical tests
Thangamani Ramalingam
 
Brm (one tailed and two tailed hypothesis)
Brm (one tailed and two tailed hypothesis)Brm (one tailed and two tailed hypothesis)
Brm (one tailed and two tailed hypothesis)
Upama Dwivedi
 
Experimental Research Design (True, Quasi and Pre Experimental Design)
Experimental Research Design (True, Quasi and Pre Experimental Design)Experimental Research Design (True, Quasi and Pre Experimental Design)
Experimental Research Design (True, Quasi and Pre Experimental Design)
Alam Nuzhathalam
 
Frequency distribution
Frequency distributionFrequency distribution
Frequency distribution
Aishwarya PT
 
Statistical table
Statistical tableStatistical table
Statistical table
Afra Fathima
 
DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test
sai prakash
 
Types of data and graphical representation
Types of data and graphical representationTypes of data and graphical representation
Types of data and graphical representation
Reena Titoria
 
Type i and type ii errors
Type i and type ii errorsType i and type ii errors
Type i and type ii errors
p24ssp
 
Non – Probability Sampling (Convenience, Purposive).
Non – Probability Sampling (Convenience, Purposive).Non – Probability Sampling (Convenience, Purposive).
Non – Probability Sampling (Convenience, Purposive).
Vikas Kumar
 
coefficient correlation
coefficient correlation coefficient correlation
coefficient correlation
irshad narejo
 
Quick reminder ordinal or scaled or nominal porportional
Quick reminder ordinal or scaled or nominal porportionalQuick reminder ordinal or scaled or nominal porportional
Quick reminder ordinal or scaled or nominal porportional
Ken Plummer
 
Scales of measurement in statistics
Scales of measurement in statisticsScales of measurement in statistics
Scales of measurement in statistics
Shahid Imran Khan
 
Ad

More Related Content

What's hot (20)

Presentation of Data (thesis writing)
Presentation of Data (thesis writing)Presentation of Data (thesis writing)
Presentation of Data (thesis writing)
Charlene Eve Saligumba
 
Wilcoxon signed rank test
Wilcoxon signed rank testWilcoxon signed rank test
Wilcoxon signed rank test
Biswash Sapkota
 
Types of Variables
Types of VariablesTypes of Variables
Types of Variables
Ali Mustafa
 
wilcoxon signed rank test
wilcoxon signed rank testwilcoxon signed rank test
wilcoxon signed rank test
raj shekar
 
Confirmatory Factor Analysis
Confirmatory Factor AnalysisConfirmatory Factor Analysis
Confirmatory Factor Analysis
Economic Research Forum
 
Analysis of variance (anova)
Analysis of variance (anova)Analysis of variance (anova)
Analysis of variance (anova)
Sadhana Singh
 
Point biserial correlation example
Point biserial correlation examplePoint biserial correlation example
Point biserial correlation example
Muhammad Khalil
 
Threats to internal and external validity
Threats to internal and external validityThreats to internal and external validity
Threats to internal and external validity
rodsazon
 
Experimental Design | Statistics
Experimental Design | StatisticsExperimental Design | Statistics
Experimental Design | Statistics
Transweb Global Inc
 
Parametric vs Non-Parametric
Parametric vs Non-ParametricParametric vs Non-Parametric
Parametric vs Non-Parametric
Aniruddha Deshmukh
 
Statistical tests
Statistical tests Statistical tests
Statistical tests
Thangamani Ramalingam
 
Brm (one tailed and two tailed hypothesis)
Brm (one tailed and two tailed hypothesis)Brm (one tailed and two tailed hypothesis)
Brm (one tailed and two tailed hypothesis)
Upama Dwivedi
 
Experimental Research Design (True, Quasi and Pre Experimental Design)
Experimental Research Design (True, Quasi and Pre Experimental Design)Experimental Research Design (True, Quasi and Pre Experimental Design)
Experimental Research Design (True, Quasi and Pre Experimental Design)
Alam Nuzhathalam
 
Frequency distribution
Frequency distributionFrequency distribution
Frequency distribution
Aishwarya PT
 
Statistical table
Statistical tableStatistical table
Statistical table
Afra Fathima
 
DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test
sai prakash
 
Types of data and graphical representation
Types of data and graphical representationTypes of data and graphical representation
Types of data and graphical representation
Reena Titoria
 
Type i and type ii errors
Type i and type ii errorsType i and type ii errors
Type i and type ii errors
p24ssp
 
Non – Probability Sampling (Convenience, Purposive).
Non – Probability Sampling (Convenience, Purposive).Non – Probability Sampling (Convenience, Purposive).
Non – Probability Sampling (Convenience, Purposive).
Vikas Kumar
 
coefficient correlation
coefficient correlation coefficient correlation
coefficient correlation
irshad narejo
 
Presentation of Data (thesis writing)
Presentation of Data (thesis writing)Presentation of Data (thesis writing)
Presentation of Data (thesis writing)
Charlene Eve Saligumba
 
Wilcoxon signed rank test
Wilcoxon signed rank testWilcoxon signed rank test
Wilcoxon signed rank test
Biswash Sapkota
 
Types of Variables
Types of VariablesTypes of Variables
Types of Variables
Ali Mustafa
 
wilcoxon signed rank test
wilcoxon signed rank testwilcoxon signed rank test
wilcoxon signed rank test
raj shekar
 
Confirmatory Factor Analysis
Confirmatory Factor AnalysisConfirmatory Factor Analysis
Confirmatory Factor Analysis
Economic Research Forum
 
Analysis of variance (anova)
Analysis of variance (anova)Analysis of variance (anova)
Analysis of variance (anova)
Sadhana Singh
 
Point biserial correlation example
Point biserial correlation examplePoint biserial correlation example
Point biserial correlation example
Muhammad Khalil
 
Threats to internal and external validity
Threats to internal and external validityThreats to internal and external validity
Threats to internal and external validity
rodsazon
 
Experimental Design | Statistics
Experimental Design | StatisticsExperimental Design | Statistics
Experimental Design | Statistics
Transweb Global Inc
 
Parametric vs Non-Parametric
Parametric vs Non-ParametricParametric vs Non-Parametric
Parametric vs Non-Parametric
Aniruddha Deshmukh
 
Statistical tests
Statistical tests Statistical tests
Statistical tests
Thangamani Ramalingam
 
Brm (one tailed and two tailed hypothesis)
Brm (one tailed and two tailed hypothesis)Brm (one tailed and two tailed hypothesis)
Brm (one tailed and two tailed hypothesis)
Upama Dwivedi
 
Experimental Research Design (True, Quasi and Pre Experimental Design)
Experimental Research Design (True, Quasi and Pre Experimental Design)Experimental Research Design (True, Quasi and Pre Experimental Design)
Experimental Research Design (True, Quasi and Pre Experimental Design)
Alam Nuzhathalam
 
Frequency distribution
Frequency distributionFrequency distribution
Frequency distribution
Aishwarya PT
 
Statistical table
Statistical tableStatistical table
Statistical table
Afra Fathima
 
DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test DIstinguish between Parametric vs nonparametric test
DIstinguish between Parametric vs nonparametric test
sai prakash
 
Types of data and graphical representation
Types of data and graphical representationTypes of data and graphical representation
Types of data and graphical representation
Reena Titoria
 
Type i and type ii errors
Type i and type ii errorsType i and type ii errors
Type i and type ii errors
p24ssp
 
Non – Probability Sampling (Convenience, Purposive).
Non – Probability Sampling (Convenience, Purposive).Non – Probability Sampling (Convenience, Purposive).
Non – Probability Sampling (Convenience, Purposive).
Vikas Kumar
 
coefficient correlation
coefficient correlation coefficient correlation
coefficient correlation
irshad narejo
 

Viewers also liked (20)

Quick reminder ordinal or scaled or nominal porportional
Quick reminder ordinal or scaled or nominal porportionalQuick reminder ordinal or scaled or nominal porportional
Quick reminder ordinal or scaled or nominal porportional
Ken Plummer
 
Scales of measurement in statistics
Scales of measurement in statisticsScales of measurement in statistics
Scales of measurement in statistics
Shahid Imran Khan
 
Burns And Bush Chapter 15
Burns And Bush Chapter 15Burns And Bush Chapter 15
Burns And Bush Chapter 15
Largest Catholic University
 
Workplace harassment of health worker
Workplace harassment of health workerWorkplace harassment of health worker
Workplace harassment of health worker
Shahid Imran Khan
 
Basic Statistics
Basic StatisticsBasic Statistics
Basic Statistics
Chie Pegollo
 
Descriptive v inferential
Descriptive v inferentialDescriptive v inferential
Descriptive v inferential
Ken Plummer
 
Inferential statistics (2)
Inferential statistics (2)Inferential statistics (2)
Inferential statistics (2)
rajnulada
 
EEX 501 Assess Ch4,5,6,7,All
EEX 501 Assess Ch4,5,6,7,AllEEX 501 Assess Ch4,5,6,7,All
EEX 501 Assess Ch4,5,6,7,All
M, Michelle Jeannite
 
Basic Statistics & Data Analysis
Basic Statistics & Data AnalysisBasic Statistics & Data Analysis
Basic Statistics & Data Analysis
Ajendra Sharma
 
K&n's
K&n'sK&n's
K&n's
Mona Sajid
 
Intro to quant_analysis_students
Intro to quant_analysis_studentsIntro to quant_analysis_students
Intro to quant_analysis_students
mstegman
 
Is the Data Scaled, Ordinal, or Nominal Proportional?
Is the Data Scaled, Ordinal, or Nominal Proportional?Is the Data Scaled, Ordinal, or Nominal Proportional?
Is the Data Scaled, Ordinal, or Nominal Proportional?
Ken Plummer
 
Standard Deviation
Standard DeviationStandard Deviation
Standard Deviation
JRisi
 
Quickreminder nature of the data (relationship)
Quickreminder nature of the data (relationship)Quickreminder nature of the data (relationship)
Quickreminder nature of the data (relationship)
Ken Plummer
 
Tutorial parametric v. non-parametric
Tutorial parametric v. non-parametricTutorial parametric v. non-parametric
Tutorial parametric v. non-parametric
Ken Plummer
 
Reporting a single sample t-test
Reporting a single sample t-testReporting a single sample t-test
Reporting a single sample t-test
Ken Plummer
 
Null hypothesis for single linear regression
Null hypothesis for single linear regressionNull hypothesis for single linear regression
Null hypothesis for single linear regression
Ken Plummer
 
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
Ken Plummer
 
Measurement scales (1)
Measurement scales (1)Measurement scales (1)
Measurement scales (1)
Ken Plummer
 
Inferential statistics.ppt
Inferential statistics.pptInferential statistics.ppt
Inferential statistics.ppt
Nursing Path
 
Quick reminder ordinal or scaled or nominal porportional
Quick reminder ordinal or scaled or nominal porportionalQuick reminder ordinal or scaled or nominal porportional
Quick reminder ordinal or scaled or nominal porportional
Ken Plummer
 
Scales of measurement in statistics
Scales of measurement in statisticsScales of measurement in statistics
Scales of measurement in statistics
Shahid Imran Khan
 
Burns And Bush Chapter 15
Burns And Bush Chapter 15Burns And Bush Chapter 15
Burns And Bush Chapter 15
Largest Catholic University
 
Workplace harassment of health worker
Workplace harassment of health workerWorkplace harassment of health worker
Workplace harassment of health worker
Shahid Imran Khan
 
Basic Statistics
Basic StatisticsBasic Statistics
Basic Statistics
Chie Pegollo
 
Descriptive v inferential
Descriptive v inferentialDescriptive v inferential
Descriptive v inferential
Ken Plummer
 
Inferential statistics (2)
Inferential statistics (2)Inferential statistics (2)
Inferential statistics (2)
rajnulada
 
EEX 501 Assess Ch4,5,6,7,All
EEX 501 Assess Ch4,5,6,7,AllEEX 501 Assess Ch4,5,6,7,All
EEX 501 Assess Ch4,5,6,7,All
M, Michelle Jeannite
 
Basic Statistics & Data Analysis
Basic Statistics & Data AnalysisBasic Statistics & Data Analysis
Basic Statistics & Data Analysis
Ajendra Sharma
 
K&n's
K&n'sK&n's
K&n's
Mona Sajid
 
Intro to quant_analysis_students
Intro to quant_analysis_studentsIntro to quant_analysis_students
Intro to quant_analysis_students
mstegman
 
Is the Data Scaled, Ordinal, or Nominal Proportional?
Is the Data Scaled, Ordinal, or Nominal Proportional?Is the Data Scaled, Ordinal, or Nominal Proportional?
Is the Data Scaled, Ordinal, or Nominal Proportional?
Ken Plummer
 
Standard Deviation
Standard DeviationStandard Deviation
Standard Deviation
JRisi
 
Quickreminder nature of the data (relationship)
Quickreminder nature of the data (relationship)Quickreminder nature of the data (relationship)
Quickreminder nature of the data (relationship)
Ken Plummer
 
Tutorial parametric v. non-parametric
Tutorial parametric v. non-parametricTutorial parametric v. non-parametric
Tutorial parametric v. non-parametric
Ken Plummer
 
Reporting a single sample t-test
Reporting a single sample t-testReporting a single sample t-test
Reporting a single sample t-test
Ken Plummer
 
Null hypothesis for single linear regression
Null hypothesis for single linear regressionNull hypothesis for single linear regression
Null hypothesis for single linear regression
Ken Plummer
 
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
Quick reminder diff-rel-ind-gd of fit (spanish in four slides) (2)
Ken Plummer
 
Measurement scales (1)
Measurement scales (1)Measurement scales (1)
Measurement scales (1)
Ken Plummer
 
Inferential statistics.ppt
Inferential statistics.pptInferential statistics.ppt
Inferential statistics.ppt
Nursing Path
 
Ad

More from Ken Plummer (20)

Diff rel gof-fit - jejit - practice (5)
Diff rel gof-fit - jejit - practice (5)Diff rel gof-fit - jejit - practice (5)
Diff rel gof-fit - jejit - practice (5)
Ken Plummer
 
Learn About Range - Copyright updated
Learn About Range - Copyright updatedLearn About Range - Copyright updated
Learn About Range - Copyright updated
Ken Plummer
 
Inferential vs descriptive tutorial of when to use - Copyright Updated
Inferential vs descriptive tutorial of when to use - Copyright UpdatedInferential vs descriptive tutorial of when to use - Copyright Updated
Inferential vs descriptive tutorial of when to use - Copyright Updated
Ken Plummer
 
Diff rel ind-fit practice - Copyright Updated
Diff rel ind-fit practice - Copyright UpdatedDiff rel ind-fit practice - Copyright Updated
Diff rel ind-fit practice - Copyright Updated
Ken Plummer
 
Normal or skewed distributions (inferential) - Copyright updated
Normal or skewed distributions (inferential) - Copyright updatedNormal or skewed distributions (inferential) - Copyright updated
Normal or skewed distributions (inferential) - Copyright updated
Ken Plummer
 
Normal or skewed distributions (descriptive both2) - Copyright updated
Normal or skewed distributions (descriptive both2) - Copyright updatedNormal or skewed distributions (descriptive both2) - Copyright updated
Normal or skewed distributions (descriptive both2) - Copyright updated
Ken Plummer
 
Nature of the data practice - Copyright updated
Nature of the data practice - Copyright updatedNature of the data practice - Copyright updated
Nature of the data practice - Copyright updated
Ken Plummer
 
Nature of the data (spread) - Copyright updated
Nature of the data (spread) - Copyright updatedNature of the data (spread) - Copyright updated
Nature of the data (spread) - Copyright updated
Ken Plummer
 
Mode practice 1 - Copyright updated
Mode practice 1 - Copyright updatedMode practice 1 - Copyright updated
Mode practice 1 - Copyright updated
Ken Plummer
 
Nature of the data (descriptive) - Copyright updated
Nature of the data (descriptive) - Copyright updatedNature of the data (descriptive) - Copyright updated
Nature of the data (descriptive) - Copyright updated
Ken Plummer
 
Dichotomous or scaled
Dichotomous or scaledDichotomous or scaled
Dichotomous or scaled
Ken Plummer
 
Skewed less than 30 (ties)
Skewed less than 30 (ties)Skewed less than 30 (ties)
Skewed less than 30 (ties)
Ken Plummer
 
Skewed sample size less than 30
Skewed sample size less than 30Skewed sample size less than 30
Skewed sample size less than 30
Ken Plummer
 
Ordinal (ties)
Ordinal (ties)Ordinal (ties)
Ordinal (ties)
Ken Plummer
 
Ordinal and nominal
Ordinal and nominalOrdinal and nominal
Ordinal and nominal
Ken Plummer
 
Relationship covariates
Relationship covariatesRelationship covariates
Relationship covariates
Ken Plummer
 
Relationship nature of data
Relationship nature of dataRelationship nature of data
Relationship nature of data
Ken Plummer
 
Number of variables (predictive)
Number of variables (predictive)Number of variables (predictive)
Number of variables (predictive)
Ken Plummer
 
Levels of the iv
Levels of the ivLevels of the iv
Levels of the iv
Ken Plummer
 
Independent variables (2)
Independent variables (2)Independent variables (2)
Independent variables (2)
Ken Plummer
 
Diff rel gof-fit - jejit - practice (5)
Diff rel gof-fit - jejit - practice (5)Diff rel gof-fit - jejit - practice (5)
Diff rel gof-fit - jejit - practice (5)
Ken Plummer
 
Learn About Range - Copyright updated
Learn About Range - Copyright updatedLearn About Range - Copyright updated
Learn About Range - Copyright updated
Ken Plummer
 
Inferential vs descriptive tutorial of when to use - Copyright Updated
Inferential vs descriptive tutorial of when to use - Copyright UpdatedInferential vs descriptive tutorial of when to use - Copyright Updated
Inferential vs descriptive tutorial of when to use - Copyright Updated
Ken Plummer
 
Diff rel ind-fit practice - Copyright Updated
Diff rel ind-fit practice - Copyright UpdatedDiff rel ind-fit practice - Copyright Updated
Diff rel ind-fit practice - Copyright Updated
Ken Plummer
 
Normal or skewed distributions (inferential) - Copyright updated
Normal or skewed distributions (inferential) - Copyright updatedNormal or skewed distributions (inferential) - Copyright updated
Normal or skewed distributions (inferential) - Copyright updated
Ken Plummer
 
Normal or skewed distributions (descriptive both2) - Copyright updated
Normal or skewed distributions (descriptive both2) - Copyright updatedNormal or skewed distributions (descriptive both2) - Copyright updated
Normal or skewed distributions (descriptive both2) - Copyright updated
Ken Plummer
 
Nature of the data practice - Copyright updated
Nature of the data practice - Copyright updatedNature of the data practice - Copyright updated
Nature of the data practice - Copyright updated
Ken Plummer
 
Nature of the data (spread) - Copyright updated
Nature of the data (spread) - Copyright updatedNature of the data (spread) - Copyright updated
Nature of the data (spread) - Copyright updated
Ken Plummer
 
Mode practice 1 - Copyright updated
Mode practice 1 - Copyright updatedMode practice 1 - Copyright updated
Mode practice 1 - Copyright updated
Ken Plummer
 
Nature of the data (descriptive) - Copyright updated
Nature of the data (descriptive) - Copyright updatedNature of the data (descriptive) - Copyright updated
Nature of the data (descriptive) - Copyright updated
Ken Plummer
 
Dichotomous or scaled
Dichotomous or scaledDichotomous or scaled
Dichotomous or scaled
Ken Plummer
 
Skewed less than 30 (ties)
Skewed less than 30 (ties)Skewed less than 30 (ties)
Skewed less than 30 (ties)
Ken Plummer
 
Skewed sample size less than 30
Skewed sample size less than 30Skewed sample size less than 30
Skewed sample size less than 30
Ken Plummer
 
Ordinal (ties)
Ordinal (ties)Ordinal (ties)
Ordinal (ties)
Ken Plummer
 
Ordinal and nominal
Ordinal and nominalOrdinal and nominal
Ordinal and nominal
Ken Plummer
 
Relationship covariates
Relationship covariatesRelationship covariates
Relationship covariates
Ken Plummer
 
Relationship nature of data
Relationship nature of dataRelationship nature of data
Relationship nature of data
Ken Plummer
 
Number of variables (predictive)
Number of variables (predictive)Number of variables (predictive)
Number of variables (predictive)
Ken Plummer
 
Levels of the iv
Levels of the ivLevels of the iv
Levels of the iv
Ken Plummer
 
Independent variables (2)
Independent variables (2)Independent variables (2)
Independent variables (2)
Ken Plummer
 
Ad

Recently uploaded (20)

SCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptx
SCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptxSCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptx
SCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptx
Ronisha Das
 
PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)
PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)
PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)
Dr. Nasir Mustafa
 
Tax evasion, Tax planning & Tax avoidance.pptx
Tax evasion, Tax planning & Tax avoidance.pptxTax evasion, Tax planning & Tax avoidance.pptx
Tax evasion, Tax planning & Tax avoidance.pptx
manishbaidya2017
 
Ancient Stone Sculptures of India: As a Source of Indian History
Ancient Stone Sculptures of India: As a Source of Indian HistoryAncient Stone Sculptures of India: As a Source of Indian History
Ancient Stone Sculptures of India: As a Source of Indian History
Virag Sontakke
 
How to Manage Upselling in Odoo 18 Sales
How to Manage Upselling in Odoo 18 SalesHow to Manage Upselling in Odoo 18 Sales
How to Manage Upselling in Odoo 18 Sales
Celine George
 
How to Manage Purchase Alternatives in Odoo 18
How to Manage Purchase Alternatives in Odoo 18How to Manage Purchase Alternatives in Odoo 18
How to Manage Purchase Alternatives in Odoo 18
Celine George
 
Kenan Fellows Participants, Projects 2025-26 Cohort
Kenan Fellows Participants, Projects 2025-26 CohortKenan Fellows Participants, Projects 2025-26 Cohort
Kenan Fellows Participants, Projects 2025-26 Cohort
EducationNC
 
All About the 990 Unlocking Its Mysteries and Its Power.pdf
All About the 990 Unlocking Its Mysteries and Its Power.pdfAll About the 990 Unlocking Its Mysteries and Its Power.pdf
All About the 990 Unlocking Its Mysteries and Its Power.pdf
TechSoup
 
Bridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast Brooklyn
Bridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast BrooklynBridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast Brooklyn
Bridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast Brooklyn
i4jd41bk
 
CNS infections (encephalitis, meningitis & Brain abscess
CNS infections (encephalitis, meningitis & Brain abscessCNS infections (encephalitis, meningitis & Brain abscess
CNS infections (encephalitis, meningitis & Brain abscess
Mohamed Rizk Khodair
 
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptx
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptxLecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptx
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptx
Arshad Shaikh
 
Cultivation Practice of Onion in Nepal.pptx
Cultivation Practice of Onion in Nepal.pptxCultivation Practice of Onion in Nepal.pptx
Cultivation Practice of Onion in Nepal.pptx
UmeshTimilsina1
 
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
Nguyen Thanh Tu Collection
 
Ajanta Paintings: Study as a Source of History
Ajanta Paintings: Study as a Source of HistoryAjanta Paintings: Study as a Source of History
Ajanta Paintings: Study as a Source of History
Virag Sontakke
 
How to Create Kanban View in Odoo 18 - Odoo Slides
How to Create Kanban View in Odoo 18 - Odoo SlidesHow to Create Kanban View in Odoo 18 - Odoo Slides
How to Create Kanban View in Odoo 18 - Odoo Slides
Celine George
 
apa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdfapa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdf
Ishika Ghosh
 
Exercise Physiology MCQS By DR. NASIR MUSTAFA
Exercise Physiology MCQS By DR. NASIR MUSTAFAExercise Physiology MCQS By DR. NASIR MUSTAFA
Exercise Physiology MCQS By DR. NASIR MUSTAFA
Dr. Nasir Mustafa
 
How to Configure Public Holidays & Mandatory Days in Odoo 18
How to Configure Public Holidays & Mandatory Days in Odoo 18How to Configure Public Holidays & Mandatory Days in Odoo 18
How to Configure Public Holidays & Mandatory Days in Odoo 18
Celine George
 
03#UNTAGGED. Generosity in architecture.
03#UNTAGGED. Generosity in architecture.03#UNTAGGED. Generosity in architecture.
03#UNTAGGED. Generosity in architecture.
MCH
 
dynastic art of the Pallava dynasty south India
dynastic art of the Pallava dynasty south Indiadynastic art of the Pallava dynasty south India
dynastic art of the Pallava dynasty south India
PrachiSontakke5
 
SCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptx
SCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptxSCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptx
SCI BIZ TECH QUIZ (OPEN) PRELIMS XTASY 2025.pptx
Ronisha Das
 
PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)
PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)
PHYSIOLOGY MCQS By DR. NASIR MUSTAFA (PHYSIOLOGY)
Dr. Nasir Mustafa
 
Tax evasion, Tax planning & Tax avoidance.pptx
Tax evasion, Tax planning & Tax avoidance.pptxTax evasion, Tax planning & Tax avoidance.pptx
Tax evasion, Tax planning & Tax avoidance.pptx
manishbaidya2017
 
Ancient Stone Sculptures of India: As a Source of Indian History
Ancient Stone Sculptures of India: As a Source of Indian HistoryAncient Stone Sculptures of India: As a Source of Indian History
Ancient Stone Sculptures of India: As a Source of Indian History
Virag Sontakke
 
How to Manage Upselling in Odoo 18 Sales
How to Manage Upselling in Odoo 18 SalesHow to Manage Upselling in Odoo 18 Sales
How to Manage Upselling in Odoo 18 Sales
Celine George
 
How to Manage Purchase Alternatives in Odoo 18
How to Manage Purchase Alternatives in Odoo 18How to Manage Purchase Alternatives in Odoo 18
How to Manage Purchase Alternatives in Odoo 18
Celine George
 
Kenan Fellows Participants, Projects 2025-26 Cohort
Kenan Fellows Participants, Projects 2025-26 CohortKenan Fellows Participants, Projects 2025-26 Cohort
Kenan Fellows Participants, Projects 2025-26 Cohort
EducationNC
 
All About the 990 Unlocking Its Mysteries and Its Power.pdf
All About the 990 Unlocking Its Mysteries and Its Power.pdfAll About the 990 Unlocking Its Mysteries and Its Power.pdf
All About the 990 Unlocking Its Mysteries and Its Power.pdf
TechSoup
 
Bridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast Brooklyn
Bridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast BrooklynBridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast Brooklyn
Bridging the Transit Gap: Equity Drive Feeder Bus Design for Southeast Brooklyn
i4jd41bk
 
CNS infections (encephalitis, meningitis & Brain abscess
CNS infections (encephalitis, meningitis & Brain abscessCNS infections (encephalitis, meningitis & Brain abscess
CNS infections (encephalitis, meningitis & Brain abscess
Mohamed Rizk Khodair
 
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptx
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptxLecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptx
Lecture 2 CLASSIFICATION OF PHYLUM ARTHROPODA UPTO CLASSES & POSITION OF_1.pptx
Arshad Shaikh
 
Cultivation Practice of Onion in Nepal.pptx
Cultivation Practice of Onion in Nepal.pptxCultivation Practice of Onion in Nepal.pptx
Cultivation Practice of Onion in Nepal.pptx
UmeshTimilsina1
 
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
BỘ ĐỀ TUYỂN SINH VÀO LỚP 10 TIẾNG ANH - 25 ĐỀ THI BÁM SÁT CẤU TRÚC MỚI NHẤT, ...
Nguyen Thanh Tu Collection
 
Ajanta Paintings: Study as a Source of History
Ajanta Paintings: Study as a Source of HistoryAjanta Paintings: Study as a Source of History
Ajanta Paintings: Study as a Source of History
Virag Sontakke
 
How to Create Kanban View in Odoo 18 - Odoo Slides
How to Create Kanban View in Odoo 18 - Odoo SlidesHow to Create Kanban View in Odoo 18 - Odoo Slides
How to Create Kanban View in Odoo 18 - Odoo Slides
Celine George
 
apa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdfapa-style-referencing-visual-guide-2025.pdf
apa-style-referencing-visual-guide-2025.pdf
Ishika Ghosh
 
Exercise Physiology MCQS By DR. NASIR MUSTAFA
Exercise Physiology MCQS By DR. NASIR MUSTAFAExercise Physiology MCQS By DR. NASIR MUSTAFA
Exercise Physiology MCQS By DR. NASIR MUSTAFA
Dr. Nasir Mustafa
 
How to Configure Public Holidays & Mandatory Days in Odoo 18
How to Configure Public Holidays & Mandatory Days in Odoo 18How to Configure Public Holidays & Mandatory Days in Odoo 18
How to Configure Public Holidays & Mandatory Days in Odoo 18
Celine George
 
03#UNTAGGED. Generosity in architecture.
03#UNTAGGED. Generosity in architecture.03#UNTAGGED. Generosity in architecture.
03#UNTAGGED. Generosity in architecture.
MCH
 
dynastic art of the Pallava dynasty south India
dynastic art of the Pallava dynasty south Indiadynastic art of the Pallava dynasty south India
dynastic art of the Pallava dynasty south India
PrachiSontakke5
 

1a difference between inferential and descriptive statistics (explanation)

  • 1. Determining whether to use Inferential and Descriptive Statistics Learning Module: Explanation
  • 2. Welcome to this learning module!
  • 3. In this module you will learn the difference between
  • 5. In later modules you will then determine when a question should be answered with one or the other.
  • 7. Simply put, descriptive statistics describe the features of a data set using numerical measures.
  • 8. Simply put, descriptive statistics describe the features of a data set using numerical measures. And that’s it!
  • 9. Simply put, descriptive statistics describe the features of a data set using numerical measures. And that’s it! Here’s an example
  • 11. a fifth Grade teacher having a parent teacher conference.
  • 12. The parent has some questions about her son Bobby.
  • 13. She will base her answers off of a class data set.
  • 14. She will base her answers off of a class data set. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 15. She will base her answers off of a class data set. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data
  • 16. She will base her answers off of a class data set. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
  • 17. Here are the parent’s questions with Mrs. Graham’s answers Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 18. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What was Bobby’s score?
  • 19. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 20. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 32
  • 21. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Out of how many possible?
  • 22. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 50
  • 23. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What were the highest and lowest scores?
  • 24. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Highest 50, Lowest 31
  • 25. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What was the class average?
  • 26. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 About 42
  • 27. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What was the most common score?
  • 28. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 45
  • 29. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Look’s like Bobby needs more study time!
  • 30. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 That’s probably a good idea.
  • 31. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 32. Back to our definition: Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 33. descriptive statistics describe the features of a data set using numerical measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 34. descriptive statistics describe the features of a data set using numerical measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
  • 35. descriptive statistics describe the features of a data set using numerical measures Numerical Measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
  • 36. descriptive statistics describe the features of a data set using numerical measures Numerical Measures Mrs. Graham’s 5th Grade Class Scores on Spelling Test Highest to Lowest Score – THE RANGE Bella 43 Most Common Score – THE MODE Betty 45 Average Score – THE MEAN Bobby 32 Etc. Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Data Set
  • 37. Now for Inferential statistics! • While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
  • 38. • While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
  • 39. • While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions or INFER about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
  • 40. • While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions or INFER about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • Inferential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
  • 41. • While descriptive statistics focus on describing a data set, • Inferential statistics aims to draw conclusions or INFER about a larger group (called a population), • based on what is happening with a smaller group (called a sample). • INFERential statistics studies a statistical sample, and from this analysis is able to say something about the population from which the sample came.
  • 42. Same example, but here come a series of inferential questions from the parent.
  • 43. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 How does your class compare with other 5th grade classes: In the school?
  • 44. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 About the same
  • 45. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What about other 5th grade classes: In the district?
  • 46. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 A little better
  • 47. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What about other 5th grade classes: In the State
  • 48. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 We are higher
  • 49. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 What about other 5th grade classes: In the Country
  • 50. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 We are much higher
  • 51. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Looks like your class Maybe does not Bobby represent doesn’t the national have to population study as and is much? probably doing better.
  • 52. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31 Maybe.
  • 53. Mrs. Graham’s 5th Grade Class Scores on Spelling Test Bella 43 Betty 45 Bobby 32 Bonnie 45 Booker 38 Boston 45 Botania 50 Boyle 45 Bunder 31
  • 54. In summary - A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
  • 55. A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
  • 56. A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
  • 57. A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population.
  • 58. A descriptive study examines the data from everyone in a group or population. A descriptive study cannot generalize to other like groups or populations. An inferential study examines the data from a sample and then generalizes the conclusions about that group to a larger population. Note – inferential and descriptive studies use the SAME statistics (e.g., mean, range, mode, etc.), but as you can see – for DIFFERENT reasons!