This document provides guidelines for exploring data and assumption testing in applied statistics. It discusses descriptive statistics, normal distribution tests, and assigning practice exercises to student groups. Specifically, it explains how to generate descriptive statistics and histograms in SPSS, introduces the Kolmogorov-Smirnov normality test, and provides examples analyzing normality for intrinsic motivation scores and exam scores from different universities. Students are then assigned to groups and asked questions related to outliers, measures of central tendency, variables, distribution characteristics, within-subjects designs, statistical errors, effect sizes, standard error, p-values, z-scores, and degrees of freedom.
Research methodology - Analysis of DataThe Stockker
Processing & Analysis of Data, Data editing, Benefits of data editing, Data coding, Classification of data, CLASSIFICATION ACCORDING THE ATTRIBUTES, CLASSIFICATION ON THE BASIS OF INTERVAL, TABULATION of data, Types of tables, Graphing of data, Bar chart, Pie chart, Line graph, histogram, Polygon / ogive, Analysis of Data, Descriptive Analysis, Uni-Variate Analysis, Bivariate Analysis, Multi-Variate Analysis, Causal Analysis, Inferential Analysis, PARAMETRIC TESTS, Non parametric Test,
Research methodology - Analysis of DataThe Stockker
Processing & Analysis of Data, Data editing, Benefits of data editing, Data coding, Classification of data, CLASSIFICATION ACCORDING THE ATTRIBUTES, CLASSIFICATION ON THE BASIS OF INTERVAL, TABULATION of data, Types of tables, Graphing of data, Bar chart, Pie chart, Line graph, histogram, Polygon / ogive, Analysis of Data, Descriptive Analysis, Uni-Variate Analysis, Bivariate Analysis, Multi-Variate Analysis, Causal Analysis, Inferential Analysis, PARAMETRIC TESTS, Non parametric Test,
data processing and presentation
,
editing
,
model building
,
stages of data analysis/processing operations
,
coding
,
inferential analysis
,
classification
,
tabulation
,
analysis
,
descriptive analysis
,
fact finding
,
common research objectives for secondary data stu
,
data based marketin
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
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data processing and presentation
,
editing
,
model building
,
stages of data analysis/processing operations
,
coding
,
inferential analysis
,
classification
,
tabulation
,
analysis
,
descriptive analysis
,
fact finding
,
common research objectives for secondary data stu
,
data based marketin
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...Stats Statswork
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Get your quality homework help now and stand out.Our professional writers are committed to excellence. We have trained the best scholars in different fields of study.Contact us now at http://www.essaysexperts.net/ and place your order at affordable price done within set deadlines.We always have someone online ready to answer all your queries and take your requests.
Introduction to Statistics -
Sampling Techniques, Types of Statistics, Descriptive Statistics,
Inferential Statistics,
Variables and Types of Data: Qualitative, Quantitative, Discrete,
Continuous, Organizing and Graphing Data: Qualitative Data, Quantitative Data
Social Science Statistics STA2122.501 ● ONLINE Project 3ChereCheek752
Social Science Statistics
STA2122.501 ● ONLINE
Project 3: Comparing Global Values and Attitudes
SPSS SUPPLEMENT
Project 3 requires you to select two variables and perform an independent-sample hypothesis test using SPSS. However, access to SPSS may be
limited during this time. Therefore, I have performed four different sets of analyses you may use in your report. Below, I include a print-out of the
descriptive statistics and analyses for three (3) different scenarios (i.e., this is what you would see in SPSS if you analyzed the data yourself). You
are responsible for all other parts of the project. Please email us at the address above if you have any questions or if you would like another option.
OPTION 1: Differences in views of competition (v99) between Japan and the United States (JAPvUS) (page 2)
OPTION 2: Differences in perception of justification for man beating wife (v208) between Sweden and the United States (SWEvUS) (page 3)
OPTION 3: Differences in perception of the benefits of technology (v192) between China and the United States (CHIvUS) (page 4)
University of South Florida
Instructor: Dr. Erica L. Toothman
Email: [email protected]
OPTION 1: Differences in views of competition (v99) between Japan and the United States (JAPvUS)
Group Statistics
MEXvUS N Mean Std. Deviation
Std. Error
Mean
Competition good or
harmful
Japan 1945 3.54 2.337 .053
USA 2154 3.94 2.302 .050
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Competition good
or harmful
Equal variances
assumed
.608 .436 -5.425 4097 .000 -.393 .073 -.536 -.251
Equal variances
not assumed
-5.421 4041.146 .000 -.393 .073 -.536 -.251
OPTION 2: Differences in perception of justification for man beating wife (v208) between Sweden and the United States (SWEvUS)
Group Statistics
SWEvUS N Mean Std. Deviation
Std. Error
Mean
Justifiable: For a man to
beat his wife
Sweden 1182 1.38 1.482 .043
USA 2178 1.44 1.468 .031
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Justifiable: For a
man to beat his
wife
Equal variances
assumed
4.560 .033 -1.110 3358 .267 -.059 .053 -.163 .045
Equal variances
not assumed
-1.107 2403.460 .269 -.059 .053 -.164 .046
OPTION 3: Differences in perception of the benefits of technology (v192) between China and the United States (CHIvUS)
Group Statistics
CHIvUS N Mean Std. Deviation
Std. Error
Mean
Science and technology
are making our lives
healthier, easier, and
more comfortable
China 1842 8.33 1.697 .040
USA 2163 7.28 1.957 .042
Independent Samples Test
Leven ...
Social Science Statistics STA2122.501 ● ONLINE Project 3.docxrosemariebrayshaw
Social Science Statistics
STA2122.501 ● ONLINE
Project 3: Comparing Global Values and Attitudes
SPSS SUPPLEMENT
Project 3 requires you to select two variables and perform an independent-sample hypothesis test using SPSS. However, access to SPSS may be
limited during this time. Therefore, I have performed four different sets of analyses you may use in your report. Below, I include a print-out of the
descriptive statistics and analyses for three (3) different scenarios (i.e., this is what you would see in SPSS if you analyzed the data yourself). You
are responsible for all other parts of the project. Please email us at the address above if you have any questions or if you would like another option.
OPTION 1: Differences in views of competition (v99) between Japan and the United States (JAPvUS) (page 2)
OPTION 2: Differences in perception of justification for man beating wife (v208) between Sweden and the United States (SWEvUS) (page 3)
OPTION 3: Differences in perception of the benefits of technology (v192) between China and the United States (CHIvUS) (page 4)
University of South Florida
Instructor: Dr. Erica L. Toothman
Email: [email protected]
OPTION 1: Differences in views of competition (v99) between Japan and the United States (JAPvUS)
Group Statistics
MEXvUS N Mean Std. Deviation
Std. Error
Mean
Competition good or
harmful
Japan 1945 3.54 2.337 .053
USA 2154 3.94 2.302 .050
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Competition good
or harmful
Equal variances
assumed
.608 .436 -5.425 4097 .000 -.393 .073 -.536 -.251
Equal variances
not assumed
-5.421 4041.146 .000 -.393 .073 -.536 -.251
OPTION 2: Differences in perception of justification for man beating wife (v208) between Sweden and the United States (SWEvUS)
Group Statistics
SWEvUS N Mean Std. Deviation
Std. Error
Mean
Justifiable: For a man to
beat his wife
Sweden 1182 1.38 1.482 .043
USA 2178 1.44 1.468 .031
Independent Samples Test
Levene's Test for
Equality of Variances t-test for Equality of Means
F Sig. t df
Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
Lower Upper
Justifiable: For a
man to beat his
wife
Equal variances
assumed
4.560 .033 -1.110 3358 .267 -.059 .053 -.163 .045
Equal variances
not assumed
-1.107 2403.460 .269 -.059 .053 -.164 .046
OPTION 3: Differences in perception of the benefits of technology (v192) between China and the United States (CHIvUS)
Group Statistics
CHIvUS N Mean Std. Deviation
Std. Error
Mean
Science and technology
are making our lives
healthier, easier, and
more comfortable
China 1842 8.33 1.697 .040
USA 2163 7.28 1.957 .042
Independent Samples Test
Leven.
Pg. 05Question FiveAssignment #Deadline Day 22.docxmattjtoni51554
Pg. 05
Question Five
Assignment #
Deadline: Day 22/10/2017 @ 23:59
[Total Mark for this Assignment is 25]
System Analysis and Design
IT 243
College of Computing and Informatics
Question One
5 Marks
Learning Outcome(s):
Understand the need of Feasibility analysis in project approval and its types
What is feasibility analysis? List and briefly discuss three kinds of feasibility analysis.
Question Two
5 Marks
Learning Outcome(s):
Understand the various cost incurred in project development
How can you classify costs? Describe each cost classification and provide a typical example of each category.Question Three
5 Marks
Learning Outcome(s):
System Development Life Cycle methodologies (Waterfall & Prototyping)
There a several development methodologies for the System Development Life Cycle (SDLC). Among these are the Waterfall and System Prototyping models. Compare the two methodologies in details in terms of the following criteria.
Criteria
Waterfall
System Prototyping
Description
Requirements Clarity
System complexity
Project Time schedule
Question Four
5 Marks
Learning Outcome(s):
Understand JAD Session and its procedure
What is JAD session? Describe the five major steps in conducting JAD sessions.
Question Five
5 Marks
Learning Outcome(s):
Ability to distinguish between functional and non functional requirements
State what is meant by the functional and non-functional requirements. What are the primary types of nonfunctional requirements? Give two examples of each. What role do nonfunctional requirements play in the project overall?
# Marks
4 - PRELIMINARY DATA SCREENING
4.1 Introduction: Problems in Real Data
Real datasets often contain errors, inconsistencies in responses or measurements, outliers, and missing values. Researchers should conduct thorough preliminary data screening to identify and remedy potential problems with their data prior to running the data analyses that are of primary interest. Analyses based on a dataset that contains errors, or data that seriously violate assumptions that are required for the analysis, can yield misleading results.
Some of the potential problems with data are as follows: errors in data coding and data entry, inconsistent responses, missing values, extreme outliers, nonnormal distribution shapes, within-group sample sizes that are too small for the intended analysis, and nonlinear relations between quantitative variables. Problems with data should be identified and remedied (as adequately as possible) prior to analysis. A research report should include a summary of problems detected in the data and any remedies that were employed (such as deletion of outliers or data transformations) to address these problems.
4.2 Quality Control During Data Collection
There are many different possible methods of data collection. A psychologist may collect data on personality or attitudes by asking participants to answer questions on a questionnaire..
PUH 6301, Public Health Research 1 Course Learning OuTatianaMajor22
PUH 6301, Public Health Research 1
Course Learning Outcomes for Unit VI
Upon completion of this unit, students should be able to:
4. Evaluate strategies for data analysis to determine the best statistical tests needed for research
methods.
4.1 Determine the four levels of measurement as valid research statistical techniques in the public
health research process.
4.2 Explain why proper data and statistical analysis is important.
4.3 Describe the basic types of statistic tests.
Course/Unit
Learning Outcomes
Learning Activity
4.1
Unit Lesson
Chapter 28
Chapter 29
Chapter 30
Chapter 31
Chapter 33
Blog: “Descriptive vs. Inferential Statistics: What’s the Difference?
Unit VI Essay
4.2
Unit Lesson
Chapter 28
Unit VI Essay
4.3
Unit Lesson
Chapter 29
Unit VI Essay
Required Unit Resources
Chapter 28: Data Management
Chapter 29: Descriptive Statistics
Chapter 30: Comparative Statistics
Chapter 31: Regression Analysis
Chapter 33: Additional Analysis Tools
In order to access the following resource, click the link below:
The website below provides a good summary of how the public health researcher can use descriptive and
inferential statistics methods to conduct public health research.
Market Research Guy. (2011, December 1). Descriptive vs. inferential statistics: What’s the difference? [Blog
post]. http://www.mymarketresearchmethods.com/descriptive-inferential-statistics-difference/
UNIT VI STUDY GUIDE
Data Analysis Plan
http://www.mymarketresearchmethods.com/descriptive-inferential-statistics-difference/
PUH 6301, Public Health Research 2
UNIT x STUDY GUIDE
Title
Unit Lesson
Introduction
This unit covers the statistical procedures used to analyze the data collected from research tools. During this
stage of research, you may begin to draw conclusions and be able to answer the research question(s) and
sub-question(s) you developed in Unit I. Use statistics in this stage of research to manipulate the data and
make it understandable for others to read. Shi (2008) encourages researchers to know and understand basic
statistics and statistical procedures. The data analysis phase of research is important because it makes sense
of the data that can be used for future research studies (Jacobsen, 2021).
Data Management
Data management is the entire process of keeping a record of all the results of clinical assessments
conducted during a research study (Jacobsen, 2021). Record keeping includes listing details on potential
articles, pulling information from patient charts, tracking responses from surveys, or recording assessment
results from cohorts or studies. It is vital that those responsible for collecting and keeping data maintain
confidentiality and the integrity of data sets from all outside sources. Once researchers enter the data into the
spreadsheet or database, the data should be recoded and double-checked prior to beginning statistical
ana ...
· Toggle DrawerOverviewFor this assessment, you will complete .docxodiliagilby
· Toggle Drawer
Overview
For this assessment, you will complete an SPSS data analysis report using t-test output for assigned variables.
You will review the theory, logic, and application of t tests. The t test is a basic inferential statistic often reported in psychological research. You will discover that t tests, as well as analysis of variance (ANOVA), compare group means on some quantitative outcome variable.
SHOW LESS
By successfully completing this assessment, you will demonstrate your proficiency in the following course competencies and assessment criteria:
· Competency 1: Analyze the computation, application, strengths, and limitations of various statistical tests.
1. Develop a conclusion that includes strengths and limitations of an independent-samples t test.
. Competency 2: Analyze the decision-making process of data analysis.
2. Analyze the assumptions of the independent-samples t test.
. Competency 3: Apply knowledge of hypothesis testing.
3. Develop a research question, null hypothesis, alternative hypothesis, and alpha level.
. Competency 4: Interpret the results of statistical analyses.
4. Interpret the output of the independent-samples t test.
. Competency 5: Apply a statistical program's procedure to data.
5. Apply the appropriate SPSS procedures to check assumptions and calculate the independent-samples t test to generate relevant output.
. Competency 6: Apply the results of statistical analyses (your own or others) to your field of interest or career.
6. Develop a context for the data set, including a definition of required variables and scales of measurement.
. Competency 7: Communicate in a manner that is scholarly, professional, and consistent with the expectations for members in the identified field of study.
7. Communicate in a manner that is scholarly, professional, and consistent with the expectations for members in the identified field of study.
Competency Map
CHECK YOUR PROGRESSUse this online tool to track your performance and progress through your course.
· Toggle Drawer
Context
Read Assessment 3 Context [DOC] for important information on the following topics:
SHOW LESS
. Logic of the t test.
. Assumptions of the t test.
. Hypothesis testing for a t test.
. Effect size for a t test.
. Testing assumptions: The Shapiro-Wilk test and Levene's test.
. Proper reporting of the independent-samples t test.
. t, degrees of freedom, and t value.
. Probability value.
. Effect size.
· Toggle Drawer
Questions to Consider
As you prepare to complete this assessment, you may want to think about other related issues to deepen your understanding or broaden your viewpoint. You are encouraged to consider the questions below and discuss them with a fellow learner, a work associate, an interested friend, or a member of your professional community. Note that these questions are for your own development and exploration and do not need to be completed or submitted as part of your assessment.
SHOW LESS
Various Forms of the t Test
. In w ...
SPSS is powerful to analyze Educational data. This paper intends to support educational leaders the benefits of data analyzing with applied SPSS. It showed the data analysis of qualified rates such as bad, neutral, good and very good on the subjects. As SPSS's background algorithms, it showed the cross tabulation algorithm for cross tabulation tables. And then Sample data ‘course evaluation.sav' was downloaded from Google and was analyzed and viewed. It used IBM SPSS statistics version 23 and PYTHON version 3.7. Aung Cho | Aung Si Thu ""Educational Data Analysis by Applied SPSS"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25092.pdf
Paper URL: https://www.ijtsrd.com/computer-science/data-miining/25092/educational-data-analysis-by-applied-spss/aung-cho
Bus 308 Effective Communication - snaptutorial.comHarrisGeorg10
BUS 308 Week 2 Problem Set
BUS 308 Week 3 Problem Set (Anova)
BUS 308 Week 4 Problem Set (Regression and Correlation)
BUS 308 Week 5 Final Paper Statistics Reflection (2 Papers)
BUS 308 Week 1 DQ 1
BUS 308 Week 1 DQ 2
BUS 308 Week 2 DQ 1
BUS 308 Week 2 DQ 2
BUS 308 Week 3 DQ 1
BUS 308 Week 3 DQ 2
1. Introduction to Applied Statistics and Applied Statistical Methods Practical guidelines
Prof. Dr. Chang Zhu page 1
Table of Contents
LECTURE 2.......................................................................................................................................................... 2
EXPLORING DATA/ASSUMPTION TESTING........................................................................................................ 2
DESCRIPTIVE STATISTICS................................................................................................................................ 2
NORMAL DISTRIBUTION TEST (the Kolmogorov-Smirnov (K-S) test) ............................................................ 3
ASSIGNMENT..................................................................................................................................................... 5
2. Introduction to Applied Statistics and Applied Statistical Methods Practical guidelines
Prof. Dr. Chang Zhu page 2
LECTURE 2
EXPLORING DATA/ASSUMPTION TESTING
Before proceeding to certain kind of analysis, it is important that we should explore:
• the characteristics of our data (mean, mode, median, variance, standard deviation, and range),
• the distribution of your data, if they are normally distributed or not by (1) values of skewness and
kurtosis (SPSS also provides histograms to visualize the distribution in the Descriptive Statistics
Command) and (2) tests of normality,
• the homogeneity of variance between groups (if we are to conduct analysis for groups).
DESCRIPTIVE STATISTICS
e.g. we want to know the characteristics and distribution of our data for the variable Intrinsic
_Motivation_learn (the file named sample data 1.sav)
In SPSS, choose Analyse > Descriptive Statistics > Frequencies
Select the variable Intrinsic _Motivation_learn and move it to the Variable(s) box by clicking the
button.
Click on the Statistics button to access the dialog box and select the options of your preference. After
finishing, click Continue.
Click on the Charts button to access the Frequencies: Charts dialog box. Choose Histograms (Show normal
curve on histogram), and click Continue to finish.
On the main dialog box, click OK to run the analysis.
In the Output document, you will see a table of descriptive statistics and a histogram with curve, based on
which you will have an idea of the characteristics (visual) distribution of your data.
3. Introduction to Applied Statistics and Applied Statistical Methods Practical guidelines
Prof. Dr. Chang Zhu page 3
NORMAL DISTRIBUTION TEST (the Kolmogorov-Smirnov (K-S) test)
To compare our data with a normally distributed data with the same mean and standard deviation, we can
use the Kolmogorov-Smirnov (K-S) test and the Shapiro-Wilk test.
Ex1. we want to know if all the scores for the variable Intrinsic_Motivation_learn are different from a
normally distributed dataset.
In SPSS, choose Analyse > Descriptive Statistics > Explore
Select the variable Intrinsic_Motivation_learn and move it to the Dependent List box by clicking the
button.
Click on the Statistics button to access the dialog box and select the Descriptive button. After finishing, click
Continue.
Click on Plots and select the option Normality plots with tests, which will provide us with the Kolmogorov-
Smirnov test and the Shapiro-Wilk test and the normal Q-Q plots.
On the main dialog box, click OK to run the analysis.
On the main dialog box, click OK to run the analysis.
In the Output document, the most important table we should look at is the table labelled Tests of
Normality.
Tests of Normality
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
1-5 .189 58 .000 .902 58 .000
a. Lilliefors Significance Correction
According to the result, the Kolmogorov-Smirnov test and the Shapiro-Wilk test are highly significant,
indicating that the distribution of scores for the variable Intrinsic_Motivation_learn is significantly different
from a normal distribution. In other words, the distribution is not normal.
Ex2. Let us now look at a data set selected from Field (2009), namely SPSSexam.sav, that includes data on
student performance on SPSS exam. The data set contains four variables: exam (scores), computer
(measure of computer literacy in percent), lecture (percentage of SPSS lectures attended), numeracy (a
measure of numerical ability out of 15), and uni (the university of the participants, either Duncetown or
Sussex).
4. Introduction to Applied Statistics and Applied Statistical Methods Practical guidelines
Prof. Dr. Chang Zhu page 4
Conduct the Kolmogorov-Smirnov (K-S) test for the variable exam (scores) for each of the two groups
(Duncetown/Sussex university) and report the result by filling the missing information in the following table
and statement.
Tip: to conduct the K-S test separately for each group, we just need to move the variable uni to the Factor
List before proceeding to the next step.
Tests of Normality
University
Kolmogorov-Smirnova
Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Percentage on SPSS exam Duncetown University .106 50 _____ .972 50 _____
Sussex University .073 50 _____ .984 50 _____
a. Lilliefors Significance Correction
*. This is a lower bound of the true significance.
The percentage on the SPSS exam, D (50) = 0.10, p (</>) ___ .05 and D (50) = 0.07, p (</>) ___ .05 are
significantly (normal/non-normal) ________________ for both the Duncetown and Sussex groups,
respectively.
(Note: the test statistic for the K-S test is denoted by the letter D in papers).
5. Introduction to Applied Statistics and Applied Statistical Methods Practical guidelines
Prof. Dr. Chang Zhu page 5
ASSIGNMENT
1) Use your own data set, explore your data with descriptive statistics and normal distribution tests
for 2 scale variables. Report the results in APA format.
2) Subscribe yourself to one of the groups in Pointcarre (based on the group formation in Assignment
1). For students who worked individually, you can also join one of the groups for those assignments
that need group discussion.
To do this, access the course Introduction to Applied Statistics and Statistical Methods, click on
Course group and you will find all the possible groups that you can subscribe as a member (just
subscribe to one group).
Post your group’s answer to the given question assigned to your group as indicated below, to the
forum labelled Exploring data, under the topic Questions and Answers/basic concepts in statistics
and distributions
When you post the group’s answer to the forum, remember to mention the question, e.g.
Question: What is ..?
Answer: ….
(shortly state which part of the answer you still have doubt (if any) and need other group’s support?
Extra credit is given if you can contribute to the answers of other group (giving critical feedback or
provide more information on the issue for your peers)
Group 1: How can we deal with outliers in case we detect them?
Group 2: Under what circumstances should we be cautious about using the mean as a measure of
central tendency?
Group 3: What are dependent/independent variables? Are there any other ways to refer to
dependent/independent variables in research papers?
Group 4: What are the cut-off (limit) values for skewness and kurtosis?
Group 5: For data to be normally distributed, what characteristics it should have?
6. Introduction to Applied Statistics and Applied Statistical Methods Practical guidelines
Prof. Dr. Chang Zhu page 6
Group 6: What is within-subjects design? What are the possible problems associated with this kind
of design?
Group 7: What are Type I and Type II error in statistics?
Group 8: What is an effect size and how is it measured?
Group 10: What is the standard error of the mean (SE)? How is it calculated?
Group 11: What does a p-value generally tell us? How can p <.05 interpreted?
Group 12: What is a z-score? How is it calculated?
Group 13: In the equation to calculate the variance, we divide the sum of squared errors by the
number of participants (N) minus 1. In this case, (N – 1) is referred to as degree of freedom. Can
you explain the concept “degree of freedom”?
Group 14: In addition to the K-S test, what are some other ways to examine the distribution of a
data set?