SlideShare a Scribd company logo
1 of 9
By: Lawrence P. Avillano, LPT
A z-score (aka, a standard score)
indicates how many Standard Deviation
an element or a score is from the mean
is from the mean. A z-score can be
calculated from the following formula
• It can be found using the formula z = (x – μ) / σ, : read z is
equal to the quotient between the difference of the raw
score and mean and the standard deviation.
Where:
• z - is the z - score
• x - is the raw score
• μ - is the (population) mean, but u can use x bar too
• .σ - is the standard deviation
• A z-score less than zero (0) represents an element
less than the mean.
• A z-score greater than 0 represents an element
greater than the mean.
• A z-score equal to 0 represents an element equal to
the mean.
• A z-score equal to 1 represents an element that is 1 standard
deviation greater than the mean; a z-score equal to 2, 2
standard deviations greater than the mean; etc.
• A z-score equal to -1 represents an element that is 1 standard
deviation less than the mean; a z-score equal to -2, 2 standard
deviations less than the mean; etc.
• If the number of elements in the set is large, about 68% of the
elements have a z-score between -1 and 1; about 95% have a z-
score between -2 and 2; and about 99% have a z-score between
-3 and 3.
In a 20 item test, the average score of the class is 16 and with 3.5
SD. Cherrie Pie scored 18.
z = (x – μ) / σ
z = (18– 16) /3.5
z = 2/3.5z = 0.57
That means that Cherrie Pie`s score is 0.57 (less than 1) standard
deviations above the mean.
Other than said above, we can use Cherri Pies`s z-score of .57 to
see how well she performed compared to other students or look at
her class standing.
• We may have to look through the z-table which can be found
here http://www.statisticshowto.com/tables/z-table/
• Now take 0.57
• y - is 0.5
• x - is 0.07
We would get the z-value of 0.2157
• Multiply the z-value by 100: 0.2157*100 = 21.57
• It means that 21.57% of the class performed better than Cherrie
Pie
• Substract 21.57 from 100: 100-21.57 = 78.43
• It means that Cherrie Pie performed better than 78.43% of the
test class.
• That would help the teacher on making decisions with regards to
Cherri Pie`s performance.

More Related Content

What's hot

Statistical inference
Statistical inferenceStatistical inference
Statistical inference
Jags Jagdish
 
descriptive and inferential statistics
descriptive and inferential statisticsdescriptive and inferential statistics
descriptive and inferential statistics
Mona Sajid
 
Standard Scores
Standard ScoresStandard Scores
Standard Scores
shoffma5
 
The kolmogorov smirnov test
The kolmogorov smirnov testThe kolmogorov smirnov test
The kolmogorov smirnov test
Subhradeep Mitra
 

What's hot (20)

Statistical inference
Statistical inferenceStatistical inference
Statistical inference
 
Z test
Z testZ test
Z test
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Basic probability concept
Basic probability conceptBasic probability concept
Basic probability concept
 
Estimation
EstimationEstimation
Estimation
 
Introduction to statistics
Introduction to statisticsIntroduction to statistics
Introduction to statistics
 
statistical inference
statistical inference statistical inference
statistical inference
 
descriptive and inferential statistics
descriptive and inferential statisticsdescriptive and inferential statistics
descriptive and inferential statistics
 
Confidence Intervals
Confidence IntervalsConfidence Intervals
Confidence Intervals
 
Measures of variability
Measures of variabilityMeasures of variability
Measures of variability
 
Statistical inference: Estimation
Statistical inference: EstimationStatistical inference: Estimation
Statistical inference: Estimation
 
Standard Scores
Standard ScoresStandard Scores
Standard Scores
 
Rank correlation
Rank correlationRank correlation
Rank correlation
 
Z scores
Z scoresZ scores
Z scores
 
The kolmogorov smirnov test
The kolmogorov smirnov testThe kolmogorov smirnov test
The kolmogorov smirnov test
 
Normal and standard normal distribution
Normal and standard normal distributionNormal and standard normal distribution
Normal and standard normal distribution
 
Coefficient of variation
Coefficient of variationCoefficient of variation
Coefficient of variation
 
Sampling and sampling distributions
Sampling and sampling distributionsSampling and sampling distributions
Sampling and sampling distributions
 
Cumulative distribution
Cumulative distributionCumulative distribution
Cumulative distribution
 
t test
t testt test
t test
 

Similar to Understanding the Z- score (Application on evaluating a Learner`s performance)

O Z Scores 68 O The Normal Curve 73 O Sample and Popul.docx
O Z Scores 68 O The Normal Curve 73 O Sample and Popul.docxO Z Scores 68 O The Normal Curve 73 O Sample and Popul.docx
O Z Scores 68 O The Normal Curve 73 O Sample and Popul.docx
hopeaustin33688
 
The standard normal curve & its application in biomedical sciences
The standard normal curve & its application in biomedical sciencesThe standard normal curve & its application in biomedical sciences
The standard normal curve & its application in biomedical sciences
Abhi Manu
 
Describing quantitative data with numbers
Describing quantitative data with numbersDescribing quantitative data with numbers
Describing quantitative data with numbers
Ulster BOCES
 

Similar to Understanding the Z- score (Application on evaluating a Learner`s performance) (20)

Chapters 2 & 4
Chapters 2 & 4Chapters 2 & 4
Chapters 2 & 4
 
Chapters 2 & 4
Chapters 2 & 4Chapters 2 & 4
Chapters 2 & 4
 
Chapters 2 4
Chapters 2  4Chapters 2  4
Chapters 2 4
 
Z score
Z scoreZ score
Z score
 
M.Ed Tcs 2 seminar ppt npc to submit
M.Ed Tcs 2 seminar ppt npc   to submitM.Ed Tcs 2 seminar ppt npc   to submit
M.Ed Tcs 2 seminar ppt npc to submit
 
Normal distribution
Normal distributionNormal distribution
Normal distribution
 
The Standard Scores in Stats (1).pptx
The Standard Scores in Stats (1).pptxThe Standard Scores in Stats (1).pptx
The Standard Scores in Stats (1).pptx
 
O Z Scores 68 O The Normal Curve 73 O Sample and Popul.docx
O Z Scores 68 O The Normal Curve 73 O Sample and Popul.docxO Z Scores 68 O The Normal Curve 73 O Sample and Popul.docx
O Z Scores 68 O The Normal Curve 73 O Sample and Popul.docx
 
statistics
statisticsstatistics
statistics
 
Converting-a-Normal-Random-Variable-to-a-Standard.pptx
Converting-a-Normal-Random-Variable-to-a-Standard.pptxConverting-a-Normal-Random-Variable-to-a-Standard.pptx
Converting-a-Normal-Random-Variable-to-a-Standard.pptx
 
Zscores
ZscoresZscores
Zscores
 
zScores_HANDOUT.pdf
zScores_HANDOUT.pdfzScores_HANDOUT.pdf
zScores_HANDOUT.pdf
 
The standard normal curve & its application in biomedical sciences
The standard normal curve & its application in biomedical sciencesThe standard normal curve & its application in biomedical sciences
The standard normal curve & its application in biomedical sciences
 
Linear Correlation
Linear Correlation Linear Correlation
Linear Correlation
 
Describing quantitative data with numbers
Describing quantitative data with numbersDescribing quantitative data with numbers
Describing quantitative data with numbers
 
Lecture_Wk08.pdf
Lecture_Wk08.pdfLecture_Wk08.pdf
Lecture_Wk08.pdf
 
Day 4 normal curve and standard scores
Day 4 normal curve and standard scoresDay 4 normal curve and standard scores
Day 4 normal curve and standard scores
 
Understanding the z score
Understanding the z scoreUnderstanding the z score
Understanding the z score
 
Lecture 10.4 bt
Lecture 10.4 btLecture 10.4 bt
Lecture 10.4 bt
 
10 Must-Know Statistical Concepts for Data Scientists.docx
10 Must-Know Statistical Concepts for Data Scientists.docx10 Must-Know Statistical Concepts for Data Scientists.docx
10 Must-Know Statistical Concepts for Data Scientists.docx
 

More from Lawrence Avillano

More from Lawrence Avillano (8)

Integumentary System.pptx
 Integumentary System.pptx Integumentary System.pptx
Integumentary System.pptx
 
Using Figures of Speech - Simile and Metaphor
Using Figures of Speech - Simile and MetaphorUsing Figures of Speech - Simile and Metaphor
Using Figures of Speech - Simile and Metaphor
 
Let and cse reviewer
Let and cse reviewerLet and cse reviewer
Let and cse reviewer
 
Mga pang ukol
Mga pang ukolMga pang ukol
Mga pang ukol
 
Pagtukoy sa mga sumusuportang detalye
Pagtukoy sa mga sumusuportang detalyePagtukoy sa mga sumusuportang detalye
Pagtukoy sa mga sumusuportang detalye
 
Mga Bahagi ng Globo
Mga Bahagi ng GloboMga Bahagi ng Globo
Mga Bahagi ng Globo
 
Physics Science: Machines and Efficiency
Physics Science: Machines and EfficiencyPhysics Science: Machines and Efficiency
Physics Science: Machines and Efficiency
 
Geometry VII: How to Identify and Classify Polygons
Geometry VII: How to Identify and Classify PolygonsGeometry VII: How to Identify and Classify Polygons
Geometry VII: How to Identify and Classify Polygons
 

Recently uploaded

Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPSSpellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
AnaAcapella
 

Recently uploaded (20)

FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024FSB Advising Checklist - Orientation 2024
FSB Advising Checklist - Orientation 2024
 
How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17How to Create and Manage Wizard in Odoo 17
How to Create and Manage Wizard in Odoo 17
 
How to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POSHow to Manage Global Discount in Odoo 17 POS
How to Manage Global Discount in Odoo 17 POS
 
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdfUnit 3 Emotional Intelligence and Spiritual Intelligence.pdf
Unit 3 Emotional Intelligence and Spiritual Intelligence.pdf
 
Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)Accessible Digital Futures project (20/03/2024)
Accessible Digital Futures project (20/03/2024)
 
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdfUGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
UGC NET Paper 1 Unit 7 DATA INTERPRETATION.pdf
 
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - EnglishGraduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - English
 
VAMOS CUIDAR DO NOSSO PLANETA! .
VAMOS CUIDAR DO NOSSO PLANETA!                    .VAMOS CUIDAR DO NOSSO PLANETA!                    .
VAMOS CUIDAR DO NOSSO PLANETA! .
 
Economic Importance Of Fungi In Food Additives
Economic Importance Of Fungi In Food AdditivesEconomic Importance Of Fungi In Food Additives
Economic Importance Of Fungi In Food Additives
 
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
NO1 Top Black Magic Specialist In Lahore Black magic In Pakistan Kala Ilam Ex...
 
Interdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptxInterdisciplinary_Insights_Data_Collection_Methods.pptx
Interdisciplinary_Insights_Data_Collection_Methods.pptx
 
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptxCOMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
COMMUNICATING NEGATIVE NEWS - APPROACHES .pptx
 
Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...
Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...
Sensory_Experience_and_Emotional_Resonance_in_Gabriel_Okaras_The_Piano_and_Th...
 
How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17How to Manage Call for Tendor in Odoo 17
How to Manage Call for Tendor in Odoo 17
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
Simple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdfSimple, Complex, and Compound Sentences Exercises.pdf
Simple, Complex, and Compound Sentences Exercises.pdf
 
Introduction to TechSoup’s Digital Marketing Services and Use Cases
Introduction to TechSoup’s Digital Marketing  Services and Use CasesIntroduction to TechSoup’s Digital Marketing  Services and Use Cases
Introduction to TechSoup’s Digital Marketing Services and Use Cases
 
Model Attribute _rec_name in the Odoo 17
Model Attribute _rec_name in the Odoo 17Model Attribute _rec_name in the Odoo 17
Model Attribute _rec_name in the Odoo 17
 
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPSSpellings Wk 4 and Wk 5 for Grade 4 at CAPS
Spellings Wk 4 and Wk 5 for Grade 4 at CAPS
 
Play hard learn harder: The Serious Business of Play
Play hard learn harder:  The Serious Business of PlayPlay hard learn harder:  The Serious Business of Play
Play hard learn harder: The Serious Business of Play
 

Understanding the Z- score (Application on evaluating a Learner`s performance)

  • 1. By: Lawrence P. Avillano, LPT
  • 2. A z-score (aka, a standard score) indicates how many Standard Deviation an element or a score is from the mean is from the mean. A z-score can be calculated from the following formula
  • 3. • It can be found using the formula z = (x – μ) / σ, : read z is equal to the quotient between the difference of the raw score and mean and the standard deviation. Where: • z - is the z - score • x - is the raw score • μ - is the (population) mean, but u can use x bar too • .σ - is the standard deviation
  • 4. • A z-score less than zero (0) represents an element less than the mean. • A z-score greater than 0 represents an element greater than the mean. • A z-score equal to 0 represents an element equal to the mean.
  • 5. • A z-score equal to 1 represents an element that is 1 standard deviation greater than the mean; a z-score equal to 2, 2 standard deviations greater than the mean; etc. • A z-score equal to -1 represents an element that is 1 standard deviation less than the mean; a z-score equal to -2, 2 standard deviations less than the mean; etc. • If the number of elements in the set is large, about 68% of the elements have a z-score between -1 and 1; about 95% have a z- score between -2 and 2; and about 99% have a z-score between -3 and 3.
  • 6. In a 20 item test, the average score of the class is 16 and with 3.5 SD. Cherrie Pie scored 18. z = (x – μ) / σ z = (18– 16) /3.5 z = 2/3.5z = 0.57 That means that Cherrie Pie`s score is 0.57 (less than 1) standard deviations above the mean.
  • 7. Other than said above, we can use Cherri Pies`s z-score of .57 to see how well she performed compared to other students or look at her class standing. • We may have to look through the z-table which can be found here http://www.statisticshowto.com/tables/z-table/ • Now take 0.57 • y - is 0.5 • x - is 0.07 We would get the z-value of 0.2157
  • 8.
  • 9. • Multiply the z-value by 100: 0.2157*100 = 21.57 • It means that 21.57% of the class performed better than Cherrie Pie • Substract 21.57 from 100: 100-21.57 = 78.43 • It means that Cherrie Pie performed better than 78.43% of the test class. • That would help the teacher on making decisions with regards to Cherri Pie`s performance.