Seven different statistical tests and a process by which you can decide which to use.
The tests are:
Test for a mean,
test for a proportion,
difference of proportions,
difference of two means - independent samples,
difference of two means - paired,
chi-squared test for independence and
regression.
This video draws together videos about Helen, her brother, Luke and the choconutties.
There is a sequel to give more practice choosing and illustrations of the different types of test with hypotheses.

Views: 731437
Dr Nic's Maths and Stats

This tutorial provides an overview of statistical analyses in the social sciences. It distinguishes between descriptive and inferential statistics, discusses factors for choosing an analysis procedure, and identifies the difference between parametric and nonparametric procedures.

Views: 227177
The Doctoral Journey

The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.

Views: 50451
White Crane Education

Views: 73364
David Russell

Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5.
Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research:
Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772.
Learn more about Dr. Leslie Curry
http://publichealth.yale.edu/people/leslie_curry.profile
Learn more about the Yale Global Health Leadership Institute
http://ghli.yale.edu

Views: 158663
YaleUniversity

Views: 18503
Introduction to Research - I

1. Descriptives: 1:32
2. T test: 2:52
3. Correlation: 4:41
4. Chi square: 5:39
5. Linear regression: 6:45
This video discusses the basic statistical analytical procedures that are required for a typical bachelor's thesis. Five stats are highlighted here: descriptives, T test, correlation, Chi square, and linear regression.
For requirements on reporting stats, please refer to the appendix of your research module manuals -- Frans Swint and I wrote an instructional text on APA reporting of stats. There is no upper limit in terms of how advanced your stats should be in your bachelor's dissertation. This video covers the basic procedures and is not meant to replace the instructions of your own research supervisor. Please consult your own research advisor for specific questions regarding your data analyses.
Please LIKE this video if you enjoyed it.
Otherwise, there is a thumb-down button, too... :P
▶ Please SUBSCRIBE to see new videos (almost) every week! ◀
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The views expressed in this video are my own and do not necessarily reflect the organizations with which I am affiliated.
#RanywayzRandom #SPSS #Research

Views: 4956
Ranywayz Random

A video tutorial from the National Union of Students, introducing the principles and practice of qualitative data analysis particularly for free text comments on the National Student Survey.

Views: 8926
Kate Little

This webinar provides an overview of basic quantitative analysis, including the types of variables and statistical tests commonly used by Student Affairs professionals. Specifically discussed are the basics of Chi-squared tests, t-tests, and ANOVAs, including how to read an SPSS output for each of these tests.

Views: 18861
CSSLOhioStateU

In this lecture, I provide a very basic introduction to quantitative data analysis and statistics. We begin by defining what "data" is, what a dataset looks like, and software tools for analyzing data.

Views: 3898
David Russell

Table of Contents:
00:25 - Purpose of the Data Analysis Section
01:14 - How to Write it
03:00 - Independent Samples t-test
04:49 - Dependent Samples t-test
05:31 - ANOVA
06:57 - Hypotheses (Predictions)

Views: 3472
Dharma Jairam

This video is part of the University of Southampton, Southampton Education School, Digital Media Resources
http://www.southampton.ac.uk/education
http://www.southampton.ac.uk/~sesvideo/

Views: 196497
Southampton Education School

Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation.
0:00 Introduction to bivariate correlation
2:20 Why does SPSS provide more than one measure for correlation?
3:26 Example 1: Pearson correlation
7:54 Example 2: Spearman (rhp), Kendall's tau-b
15:26 Example 3: correlation matrix
I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation.
Watch correlation and regression: https://youtu.be/tDxeR6JT6nM
-------------------------
Correlation of 2 rodinal variables, non monotonic
This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative.
Good luck

Views: 507580
Phil Chan

How to analyze a research questionnaire data that has been collected using SPSS. The proper techniques that are based on your research objectives and hypothesis are used. The analysis of the data is done by focusing on reliability of the questionnaire. Descriptive analysis, frequencies, correlation, factor analysis and regression analysis.

Views: 27827
Knowledge Abundance

Views: 9290
David Russell

This video is meant to be used as an introductory lesson to Mini Research Writing focusing on Data Analysis and Discussion.
As this is a mini class project, some of the requirements have been made simple due to time constraints. Plus, the focus of this mini research paper is to get students familiarized to the ways of writing an academic paper and the items that needs to be included.
suitable for beginners!

Views: 21168
NurLiyana Isa

In common health care research, some hypothesis tests are more common than others. How do you decide, between the common tests, which one is the right one for your research?
Thank you to the Statistical Learning Center for their excellent video on the same topic.
https://www.youtube.com/rulIUAN0U3w

Views: 359332
Erich Goldstein

A video tutorial from the National Union of Students, introducing the basic principles of quantitative data analysis and applying them to National Student Survey data.

Views: 14763
Kate Little

Let's go on a journey through univariate analysis and learn about descriptive statistics in research!

Views: 47100
ChrisFlipp

Use simple data analysis techniques in SPSS to analyze survey questions.

Views: 823129
Claus Ebster

Statistical Analysis of Data by Dr.Shahid,PhD - Research and Thesis
Subscribe this channel and follow all the video in the playlist.If you are currently doing any research or writing a thesis please follow step by step guidelines provided in these videos hope you will get a good
supervision of your quality research.
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Views: 387
Research and Thesis

This is Chapter 10 about how to analyze qualitative data

Views: 12859
Qingwen Dong

Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.

Views: 146067
VelactionVideos

Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx
All Excel Files for All Video files: http://people.highline.edu/mgirvin/excelisfun.htm.
Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More:
(00:08) Introduction to class
(00:49) Cells, Worksheets, Workbooks, File Names
(02:54) Navigating Worksheets & Workbook
(03:58) Navigation Keys
(04:15) Keyboard move Active Sheet
(05:40) Ribbon Tabs
(06:25) Add buttons to Quick Access Tool Bar
(07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis & Charting
(08:55) Introduction to Data Analysis
(10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells
(11:16) Keyboard Enter puts content in cell and move selected cell down
(13:00) Data Type DEFAULT Alignments
(13:11) First Formula. Entering Cell References in formulas
(13:35) Keyboard Ctrl + Enter puts content in cell & keep cell selected
(14:45) Why we don’t override DEFAULT Alignments
(15:05) Keyboard Ctrl + Z is Undo
(17:05) Proper Data Sets & Raw Data
(24:21) How To Enter Data & Data Labels
(24:21) Stylistic Formatting
(26:35) AVERAGE Function
(27:31) Format Formulas Differently than Raw Data
(28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste
(29:59) Use Eraser remove Formatting Only
(29:19) Keyboard Ctrl + B adds Bold
(29:57) Excel’s Golden Rule
(31:43) Keyboard F2 puts cell in Edit Mode
(32:01) Violating Excel’s Golden Rule
(34:12) Arrow Keys to put cell references in formulas
(35:40) Full Discussion about Formulas & Formulas Elements
(37:22) SUM function Keyboard is Alt + =
(38:22) Aggregate functions
(38:50) Why we use ranges in functions
(40:56) COUNT & COUNTA functions
(42:47) Edit Formula & change cell references
(44:18) Absolute & Relative Cell References
(45:52) Use Delete Key, Not Right-click Delete
(46:40) Fill Handle & Angry Rabbit to copy formula
(47:41) Keyboard F4 Locks Cell Reference (make Absolute)
(49:45) Keyboard Tab puts content in Cell and move selected Cell to right
(50:55) Order of Operation error
(52:17) Range Finder to find formula errors
(52:34) Lock Cell Reference after you put cell in Edit Mode
(53:58) Quickly copy an edited formula down a column
(53:07) F2 key in last cell to find formula errors
(54:15) Fix incorrect range in function
(54:55) SQRT function & Fractional Exponents
(57:20) STDEV.P function
(58:10) Navigate Large Data Sets
(58:48) Keyboard Ctrl + Arrow jumps to bottom of data set
(59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range)
(01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up
(01:02:55) Counting with conditions or criteria: COUNTIFS function
(01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell
(01:05:31) Counting between an upper & lower limit with COUNTIFS
(01:07:36) COUNTIFS copied down column
(01:10:08) Joining Comparative Operator with Cell Reference in formula
(01:12:50) Data Analysis features in Excel
(01:13:44) Sorting
(01:16:59) Filtering
(01:20:39) Introduction to PivotTables
(01:23:39) Create PivotTable dialog box
(01:24:33) Dragging & dropping Fields to create PivotTable
(01:25:31) Dragging Field to Row area creates a Unique List
(01:26:17) Outline/Tabular Layout
(01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name
(01:28:12) 2nd & 3rd PivotTable examples
(01:31:23) What is a Cross Tabulated Report?
(01:33:04) Create Cross Tabulated Report w PivotTable
(01:35:05) Show PivotTable Field List
(01:36:48) How to Pivot the Report
(01:37:50) Summarize Survey Data with PivotTable.
(01:38:34) Keyboard Alt, N, V opens PivotTable dialog box
(01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN
(01:43:25) Count & Count Number calculations in a PivotTable
(01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data
(01:47:00) #1 Rule for Charts: No Chart Junk!
(01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart
(01:51:34) Create Column Chart using Recommended Chart feature
(01:53:00) Remove Field Buttons from Pivot Chart
(01:54:10) Chart Formatting Task Pane
(01:54:45) Vary Fill Color by point
(01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable
(01:56:02) Add Data Labels to Chart
(01:57:28) Copy Chart & Create Bar Chart
(01:57:48) Change Chart Type
(01:58:15) Change Gap Width.
(01:59:17) Create Pie Chart
(01:59:23) Do NOT use 3-D Pie
(01:59:42) Add % Data Labels to Pie Chart
(02:00:25) Create Line Chart From PivotTable
(02:01:20) Link Chart Tile to Cell
(02:02:20) Move a Chart
(02:02:33) Create an X-Y Scatter Chart
(02:03:35) Add Axis Labels
(02:05:27) Number Formatting to help save time
(02:07:24) Number Formatting is a Façade
(02:10:27) General Number Format
(02:10:52) Percentage Number Formatting
(02:14:03) Don’t Multiply Relative Frequency by 100
(02:17:27) Formula for % Change & End Amount

Views: 417224
ExcelIsFun

Views: 42358
Jeongeun Kim

Through real-world examples, webinar participants learn strategies for choosing appropriate outcome measures, methods for analysis and randomization, and sample sizes as well as tips for collecting the right data to answer your scientific questions.

Views: 9167
RhoInc1984

This session will provide information regarding descriptive statistics that are often used when reviewing assessment data. We will cover the statistics available in the Baseline reporting site and we will use example situations to identify which statistics should be used to answer the questions being asked. We will also provide an overview regarding levels of measurement that can help determine what types of statistics you are able to run on your data. - See more at: http://www2.campuslabs.com/support/training/basic-statistics-quantitative-analysis-i-5/#sthash.FDO5HA6i.dpuf

Views: 35113
Campus Labs

Free MATLAB Trial: https://goo.gl/yXuXnS
Request a Quote: https://goo.gl/wNKDSg
Contact Us: https://goo.gl/RjJAkE
Learn more about MATLAB: https://goo.gl/8QV7ZZ
Learn more about Simulink: https://goo.gl/nqnbLe
-------------------------------------------------------------------------
Researchers and scientists have to commonly process, visualize and analyze large amounts of data to extract patterns, identify trends and relationships between variables, prove hypothesis, etc. A variety of statistical techniques are used in this data mining and analysis process. Using a realistic data from a clinical study, we will provide an overview of the statistical analysis and visualization capabilities in the MATLAB product family.
Highlights include:
• Data management and organization
• Data filtering and visualization
• Descriptive statistics
• Hypothesis testing and ANOVA
• Regression analysis

Views: 16117
MATLAB

An introduction to exploratory data analysis that includes discussion of descriptive statistics, graphs, outliers, and robust statistics.

Views: 29959
Prof. Patrick Meyer

Practice this lesson yourself on KhanAcademy.org right now:
https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/e/types-of-statistical-studies?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Watch the next lesson: https://www.khanacademy.org/math/probability/statistical-studies/types-of-studies/v/correlation-and-causality?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistical-studies/statistical-questions/v/reasonable-samples?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Probability and statistics on Khan Academy: We dare you to go through a day in which you never consider or use probability. Did you check the weather forecast? Busted! Did you decide to go through the drive through lane vs walk in? Busted again! We are constantly creating hypotheses, making predictions, testing, and analyzing. Our lives are full of probabilities! Statistics is related to probability because much of the data we use when determining probable outcomes comes from our understanding of statistics. In these tutorials, we will cover a range of topics, some which include: independent events, dependent probability, combinatorics, hypothesis testing, descriptive statistics, random variables, probability distributions, regression, and inferential statistics. So buckle up and hop on for a wild ride. We bet you're going to be challenged AND love it!
About Khan Academy: Khan Academy offers practice exercises, instructional videos, and a personalized learning dashboard that empower learners to study at their own pace in and outside of the classroom. We tackle math, science, computer programming, history, art history, economics, and more. Our math missions guide learners from kindergarten to calculus using state-of-the-art, adaptive technology that identifies strengths and learning gaps. We've also partnered with institutions like NASA, The Museum of Modern Art, The California Academy of Sciences, and MIT to offer specialized content.
For free. For everyone. Forever. #YouCanLearnAnything
Subscribe to KhanAcademy’s Probability and Statistics channel:
https://www.youtube.com/channel/UCRXuOXLW3LcQLWvxbZiIZ0w?sub_confirmation=1
Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 169131
Khan Academy

This video covers a few topics using the data analysis tool.
After this video you should be able to:
a) Find and use data analysis on excel to calculate statistics
b) Calculate the mean, median, mode, standard deviation, range and coefficient variation on a variable set of data in excel.
c) Conduct a confidence interval in excel.
d) Complete a T-test in excel to help complete a hypothesis test.
e) Conduct a linear regression analysis output from excel and create a scatter diagram.

Views: 99863
Me ee

The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends.
The steps are also described in writing below (Click Show more):
STEP 1, reading the transcripts
1.1. Browse through all transcripts, as a whole.
1.2. Make notes about your impressions.
1.3. Read the transcripts again, one by one.
1.4. Read very carefully, line by line.
STEP 2, labeling relevant pieces
2.1. Label relevant words, phrases, sentences, or sections.
2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant.
2.3. You might decide that something is relevant to code because:
*it is repeated in several places;
*the interviewee explicitly states that it is important;
*you have read about something similar in reports, e.g. scientific articles;
*it reminds you of a theory or a concept;
*or for some other reason that you think is relevant.
You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you.
It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds.
STEP 3, decide which codes are the most important, and create categories by bringing several codes together
3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand.
3.2. You can create new codes by combining two or more codes.
3.3. You do not have to use all the codes that you created in the previous step.
3.4. In fact, many of these initial codes can now be dropped.
3.5. Keep the codes that you think are important and group them together in the way you want.
3.6. Create categories. (You can call them themes if you want.)
3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever.
3.8. Be unbiased, creative and open-minded.
3.9. Your work now, compared to the previous steps, is on a more general, abstract level. You are conceptualizing your data.
STEP 4, label categories and decide which are the most relevant and how they are connected to each other
4.1. Label the categories. Here are some examples:
Adaptation (Category)
Updating rulebook (sub-category)
Changing schedule (sub-category)
New routines (sub-category)
Seeking information (Category)
Talking to colleagues (sub-category)
Reading journals (sub-category)
Attending meetings (sub-category)
Problem solving (Category)
Locate and fix problems fast (sub-category)
Quick alarm systems (sub-category)
4.2. Describe the connections between them.
4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study.
STEP 5, some options
5.1. Decide if there is a hierarchy among the categories.
5.2. Decide if one category is more important than the other.
5.3. Draw a figure to summarize your results.
STEP 6, write up your results
6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results.
6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example:
*results from similar, previous studies published in relevant scientific journals;
*theories or concepts from your field;
*other relevant aspects.
STEP 7 Ending remark
Nb: it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.)
Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze:
*notes from participatory observations;
*documents;
*web pages;
*or other types of qualitative data.
STEP 8 Suggested reading
Alan Bryman's book: 'Social Research Methods' published by Oxford University Press.
Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE.
Text and video (including audio) © Kent Löfgren, Sweden

Views: 704714
Kent Löfgren

This A Level / IB Psychology revision video for Research Methods looks at interpreting inferential statistics.

Views: 20842
tutor2u

What can text analysis tell us about society? Corpora of news, books, and social media encode human beliefs and culture. But it is impossible for a researcher to read all of today's rapidly growing text archives. My research develops statistical text analysis methods that measure social phenomena from textual content, especially in news and social media data. For example: How do changes to public opinion appear in microblogs? What topics get censored in the Chinese Internet? What character archetypes recur in movie plots? How do geography and ethnicity affect the diffusion of new language? In order to answer these questions effectively, we must apply and develop scientific methods in statistics, computation, and linguistics. In this talk I will illustrate these methods in a project that analyzes events in international politics. Political scientists are interested in studying international relations through *event data*: time series records of who did what to whom, as described in news articles. To address this event extraction problem, we develop an unsupervised Bayesian model of semantic event classes, which learns the verbs and textual descriptions that correspond to types of diplomatic and military interactions between countries. The model uses dynamic logistic normal priors to drive the learning of semantic classes; but unlike a topic model, it leverages deeper linguistic analysis of syntactic argument structure. Using a corpus of several million news articles over 15 years, we quantitatively evaluate how well its event types match ones defined by experts in previous work, and how well its inferences about countries correspond to real-world conflict. The method also supports exploratory analysis; for example, of the recent history of Israeli-Palestinian relations.

Views: 1249
Microsoft Research

This video shows how to use SPSS to conduct a Correlation and Regression Analysis. A simple null hypothesis is tested as well. The regression equation is explained despite the result of the hypothesis conclusion.
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MORE VIDEOS:
Watch Using Excel to find the Correlation Coefficient r here: https://youtu.be/y3bgaLwdm50
Watch ANOVA in SPSS here: https://youtu.be/Bx9ry1vBbTM
Watch Sampling Distribution of Sample Means here: https://youtu.be/anGsd2l5YpM
Watch Using Excel Charts to calculate Regression Equation here: https://youtu.be/qZjTtnyaV70
Watch Using Excel to calculate Regression Equation here: https://youtu.be/LDC0p9iZY8g
Watch ANOVA in Microsoft Excel (One-Way) here: https://youtu.be/WhBkgWL3_3k
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Views: 233773
Agron Kaci

This video was recorded for a third year Midwifery module. Featuring University of Nottingham Division of Midwifery's Zoey Spendlove, filmed by the Health E-Learning and Media Team (HELM). This resource was created for University of Nottingham Students and its intended use is as a teaching aid that forms a part of the Midwifery BSc. This video is strictly for information purposes only.

Views: 513
Health E-Learning and Media - HELM UoN

Learn about managing data in Excel. These are the Video supplements for Workbook of Quantitative Tools and Techniques in Marketing, 2nd Ed. Part of a full MOOC.

Views: 4012
Tim J Smith PhD

In this video Dr. Ziene Mottiar, DIT, discusses issues around analyzing data and writing the analysing chapter. The difference between Findings and Analysis chapters is also discussed. This video is useful for anyone who is writing a dissertation or thesis.

Views: 66716
ZieneMottiar

This is a short practical guide to Qualitative Data Analysis

Views: 114856
James Woodall

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What is DESCRIPTIVE STATISTICS? What does DESCRIPTIVE STATISTICS mean? DESCRIPTIVE STATISTICS meaning - DESCRIPTIVE STATISTICS definition - DESCRIPTIVE STATISTICS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
Descriptive statistics are statistics that quantitatively describe or summarize features of a collection of information. Descriptive statistics are distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. This generally means that descriptive statistics, unlike inferential statistics, are not developed on the basis of probability theory. Even when a data analysis draws its main conclusions using inferential statistics, descriptive statistics are generally also presented. For example in papers reporting on human subjects, typically a table is included giving the overall sample size, sample sizes in important subgroups (e.g., for each treatment or exposure group), and demographic or clinical characteristics such as the average age, the proportion of subjects of each sex, the proportion of subjects with related comorbidities etc.
Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness.
Descriptive statistics provide simple summaries about the sample and about the observations that have been made. Such summaries may be either quantitative, i.e. summary statistics, or visual, i.e. simple-to-understand graphs. These summaries may either form the basis of the initial description of the data as part of a more extensive statistical analysis, or they may be sufficient in and of themselves for a particular investigation.
For example, the shooting percentage in basketball is a descriptive statistic that summarizes the performance of a player or a team. This number is the number of shots made divided by the number of shots taken. For example, a player who shoots 33% is making approximately one shot in every three. The percentage summarizes or describes multiple discrete events. Consider also the grade point average. This single number describes the general performance of a student across the range of their course experiences.
The use of descriptive and summary statistics has an extensive history and, indeed, the simple tabulation of populations and of economic data was the first way the topic of statistics appeared. More recently, a collection of summarisation techniques has been formulated under the heading of exploratory data analysis: an example of such a technique is the box plot.
In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behavior by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future.
Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). The shape of the distribution may also be described via indices such as skewness and kurtosis. Characteristics of a variable's distribution may also be depicted in graphical or tabular format, including histograms and stem-and-leaf display.

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