Small Sample Hypothesis Test
Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/t-statistic-confidence-interval?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/z-statistics-vs-t-statistics?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:
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Subscribe to KhanAcademy: https://www.youtube.com/subscription_center?add_user=khanacademy

Views: 433187
Khan Academy

( Correction - Pen was assumed name instead of auther)
T-test,
Z-test,
F-yest,
Chi square test.
For different competitive exams
Keep watching chanakya group of economics.

Views: 226894
CHANAKYA group of Economics

Population vs sample - The first step of every statistical analysis you will perform is to determine whether the data you are dealing with is a population or a sample.
A population is the collection of all items of interest to our study and is usually denoted with an uppercase N. The numbers we’ve obtained when using a population are called parameters.
A sample is a subset of the population and is denoted with a lowercase n, and the numbers we’ve obtained when working with a sample are called statistics.
Populations are hard to define and observe. On the other hand, sampling is difficult. But samples have two big advantages. First, after you have experience, it is not that hard to recognize if a sample is representative. And, second, statistical tests are designed to work with incomplete data; thus, making a small mistake while sampling is not always a problem.
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Views: 41190
365 Data Science

This video shows how to conduct a one-sample hypothesis t-test for the mean in Microsoft Excel using the built-in Data Analysis (from raw data).
How to load Data Analysis in Excel: https://youtu.be/SqpSwxJ9t2k

Views: 94364
Joshua Emmanuel

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: 748954
Dr Nic's Maths and Stats

Participate in our survey! We'll analyze the results in future episodes! (individual data will be kept anonymous). https://bit.ly/2J1zimn
Today we’re going to talk about good and bad surveys. From user feedback surveys, telephone polls, and those questionnaires at your doctors office, surveys are everywhere, but with their ease to create and distribute, they're also susceptible to bias and error. So today we’re going to talk about how to identify good and bad survey questions, and how groups (or samples) are selected to represent the entire population since it's often just not feasible to ask everyone.
Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse
Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:
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Views: 87097
CrashCourse

A video on how to calculate the sample size. Includes discussion on how the standard deviation impacts sample size too.
Like us on: http://www.facebook.com/PartyMoreStudyLess
Related Video
How to calculate Samples Size Proportions
http://youtu.be/LGFqxJdk20o

Views: 287249
statisticsfun

Paper: Statistical Inference II
Module: Small sample properties of U statistic
Content Writer: Mr Taranga Mukherjee

Views: 299
Vidya-mitra

Get the full course at: http://www.MathTutorDVD.com
The student will learn the big picture of what a hypothesis test is in statistics. We will discuss terms such as the null hypothesis, the alternate hypothesis, statistical significance of a hypothesis test, and more.
In this step-by-step statistics tutorial, the student will learn how to perform hypothesis testing in statistics by working examples and solved problems.

Views: 1320039
mathtutordvd

statisticslectures.com - where you can find free lectures, videos, and exercises, as well as get your questions answered on our forums!

Views: 356045
statslectures

Topics: Small sampling fraction, finite population correction, sampling with/without replacement

Views: 1574
Dana R Thomson

Course: Data Analysis for Astronomy and Physics
Summer term 2017 - Universität zu Köln
Lecture 6 - Hypothesis Tests for numerical variables
How to perform an HT if the sample size is small.

Views: 9
roellig01

How to perform a simple t-test in Microsoft Excel

Views: 1196539
Jim Grange

Cucudata Short Videos: SPSS Data Analysis Tutorial
Please Visit：www.cucudata.com
For More Videos and Files
Download More Data and Information
Ask Experts & Get Answers
Our Professional Team Provides a Comprehensive Data Analysis & Data Mining Services

Views: 52
spss

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: 723957
Kent Löfgren

Subject:-Home science
Paper :-H16RM - Research Methodology and Statistics for Home Science

Views: 76
Vidya-mitra

Dr. Manishika Jain in this lecture explains the meaning of Sampling & Types of Sampling
Research Methodology
Population & Sample
Systematic Sampling
Cluster Sampling
Non Probability Sampling
Convenience Sampling
Purposeful Sampling
Extreme, Typical, Critical, or Deviant Case: Rare
Intensity: Depicts interest strongly
Maximum Variation: range of nationality, profession
Homogeneous: similar sampling groups
Stratified Purposeful: Across subcategories
Mixed: Multistage which combines different sampling
Sampling Politically Important Cases
Purposeful Sampling
Purposeful Random: If sample is larger than what can be handled & help to reduce sample size
Opportunistic Sampling: Take advantage of new opportunity
Confirming (support) and Disconfirming (against) Cases
Theory Based or Operational Construct: interaction b/w human & environment
Criterion: All above 6 feet tall
Purposive: subset of large population – high level business
Snowball Sample (Chain-Referral): picks sample analogous to accumulating snow
Advantages of Sampling
Increases validity of research
Ability to generalize results to larger population
Cuts the cost of data collection
Allows speedy work with less effort
Better organization
Greater brevity
Allows comprehensive and accurate data collection
Reduces non sampling error. Sampling error is however added.
Population & Sample @2:25
Sampling @6:30
Systematic Sampling @9:25
Cluster Sampling @ 11:22
Non Probability Sampling @13:10
Convenience Sampling @15:02
Purposeful Sampling @16:16
Advantages of Sampling @22:34
#Politically #Purposeful #Methodology #Systematic #Convenience #Probability #Cluster #Population #Research #Manishika #Examrace
For IAS Psychology postal Course refer - http://www.examrace.com/IAS/IAS-FlexiPrep-Program/Postal-Courses/Examrace-IAS-Psychology-Series.htm
For NET Paper 1 postal course visit - https://www.examrace.com/CBSE-UGC-NET/CBSE-UGC-NET-FlexiPrep-Program/Postal-Courses/Examrace-CBSE-UGC-NET-Paper-I-Series.htm
types of sampling
types of sampling pdf
probability sampling
types of sampling in hindi
random sampling
cluster sampling
non probability sampling
systematic sampling

Views: 349533
Examrace

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:12) Overview of formulas for estimating Sample Size.
2. (03:00) Example to estimate sample size for Sample Mean, Xbar.
3. (04:48) Example to estimate sample size for Sample Proportion, Pbar.
4.

Views: 4998
ExcelIsFun

This video explains some of the concepts associated with t-tests. It focuses on how to do the calculations in Excel. The difference between Excel for Windows and Excel for Mac are very, very small when using the Data Analysis Toolpak.
If you have not installed the Data Analysis Toolpak (which comes free with Excel), the following video will show you how to do it.
Windows: https://www.youtube.com/watch?v=rq8VynGNAFU
Mac: https://www.youtube.com/watch?v=1R_aJ_Fli2w

Views: 738
David Dunaetz

Hypothesis Testing and P-values
Practice this yourself on Khan Academy right now: https://www.khanacademy.org/e/hypothesis-testing-with-simulations?utm_source=YTdescription&utm_medium=YTdescription&utm_campaign=YTdescription
Watch the next lesson: https://www.khanacademy.org/math/probability/statistics-inferential/hypothesis-testing/v/one-tailed-and-two-tailed-tests?utm_source=YT&utm_medium=Desc&utm_campaign=ProbabilityandStatistics
Missed the previous lesson?
https://www.khanacademy.org/math/probability/statistics-inferential/margin-of-error/v/margin-of-error-2?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 is a nonprofit with a mission to provide a free, world-class education for anyone, anywhere. We believe learners of all ages should have unlimited access to free educational content they can master at their own pace. We use intelligent software, deep data analytics and intuitive user interfaces to help students and teachers around the world. Our resources cover preschool through early college education, including math, biology, chemistry, physics, economics, finance, history, grammar and more. We offer free personalized SAT test prep in partnership with the test developer, the College Board. Khan Academy has been translated into dozens of languages, and 100 million people use our platform worldwide every year. For more information, visit www.khanacademy.org, join us on Facebook or follow us on Twitter at @khanacademy. And remember, you can learn anything.
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: 2129116
Khan Academy

How to enter and analyze questionnaire (survey) data in SPSS is illustrated in this video. Lots more Questionnaire/Survey & SPSS Videos here: https://www.udemy.com/survey-data/?couponCode=SurveyLikertVideosYT
Check out our next text, 'SPSS Cheat Sheet,' here: http://goo.gl/b8sRHa. Prime and ‘Unlimited’ members, get our text for free. (Only 4.99 otherwise, but likely to increase soon.)
Survey data
Survey data entry
Questionnaire data entry
Channel Description: https://www.youtube.com/user/statisticsinstructor
For step by step help with statistics, with a focus on SPSS. Both descriptive and inferential statistics covered. For descriptive statistics, topics covered include: mean, median, and mode in spss, standard deviation and variance in spss, bar charts in spss, histograms in spss, bivariate scatterplots in spss, stem and leaf plots in spss, frequency distribution tables in spss, creating labels in spss, sorting variables in spss, inserting variables in spss, inserting rows in spss, and modifying default options in spss. For inferential statistics, topics covered include: t tests in spss, anova in spss, correlation in spss, regression in spss, chi square in spss, and MANOVA in spss. New videos regularly posted. Subscribe today!
YouTube Channel: https://www.youtube.com/user/statisticsinstructor
Video Transcript:
In this video we'll take a look at how to enter questionnaire or survey data into SPSS and this is something that a lot of people have questions with so it's important to make sure when you're working with SPSS in particular when you're entering data from a survey that you know how to do. Let's go ahead and take a few moments to look at that. And here you see on the right-hand side of your screen I have a questionnaire, a very short sample questionnaire that I want to enter into SPSS so we're going to create a data file and in this questionnaire here I've made a few modifications. I've underlined some variable names here and I'll talk about that more in a minute and I also put numbers in parentheses to the right of these different names and I'll also explain that as well. Now normally when someone sees this survey we wouldn't have gender underlined for example nor would we have these numbers to the right of male and female. So that's just for us, to help better understand how to enter these data. So let's go ahead and get started here. In SPSS the first thing we need to do is every time we have a possible answer such as male or female we need to create a variable in SPSS that will hold those different answers. So our first variable needs to be gender and that's why that's underlined there just to assist us as we're doing this. So we want to make sure we're in the Variable View tab and then in the first row here under Name we want to type gender and then press ENTER and that creates the variable gender. Now notice here I have two options: male and female. So when people respond or circle or check here that they're male, I need to enter into SPSS some number to indicate that. So we always want to enter numbers whenever possible into SPSS because SPSS for the vast majority of analyses performs statistical analyses on numbers not on words. So I wouldn't want and enter male, female, and so forth. I want to enter one's, two's and so on. So notice here I just arbitrarily decided males get a 1 and females get a 2. It could have been the other way around but since male was the first name listed I went and gave that 1 and then for females I gave a 2. So what we want to do in our data file here is go head and go to Values, this column, click on the None cell, notice these three dots appear they're called an ellipsis, click on that and then our first value notice here 1 is male so Value of 1 and then type Label Male and then click Add. And then our second value of 2 is for females so go ahead and enter 2 for Value and then Female, click Add and then we're done with that you want to see both of them down here and that looks good so click OK. Now those labels are in here and I'll show you how that works when we enter some numbers in a minute. OK next we have ethnicity so I'm going to call this variable ethnicity. So go ahead and type that in press ENTER and then we're going to the same thing we're going to create value labels here so 1 is African-American, 2 is Asian-American, and so on. And I'll just do that very quickly so going to Values column, click on the ellipsis. For 1 we have African American, for 2 Asian American, 3 is Caucasian, and just so you can see that here 3 is Caucasian, 4 is Hispanic, and other is 5, so let's go ahead and finish that. Four is Hispanic, 5 is other, so let's go to do that 5 is other. OK and that's it for that variable. Now we do have it says please state I'll talk about that next that's important when they can enter text we have to handle that differently.

Views: 556653
Quantitative Specialists

"What Growers Expect from New Product Introductions and Claims", Dr. Kater Hake, Vice President Agricultural & Environmental Research, Cotton Incorporated.
Hot Topics 2016 Workshop: Small-Sample-Size Statistics in Agriculture; How to Maximize Business Value, November 3, 2016 Knoxville, TN

Views: 163
Phenotype Screening Corporation

There is a mistake at 9.22. Alpha is normally set to 0.05 NOT 0.5. Thank you Victoria for bringing this to my attention.
This video reviews key terminology relating to type I and II errors along with examples. Then considerations of Power, Effect Size, Significance and Power Analysis in Quantitative Research are briefly reviewed. http://youstudynursing.com/
Research eBook on Amazon: http://amzn.to/1hB2eBd
Check out the links below and SUBSCRIBE for more youtube.com/user/NurseKillam
Quantitative research is driven by research questions and hypotheses. For every hypothesis there is an unstated null hypothesis. The null hypothesis does not need to be explicitly stated because it is always the opposite of the hypothesis. In order to demonstrate that a hypothesis is likely true researchers need to compare it to the opposite situation. The research hypothesis will be about some kind of relationship between variables. The null hypothesis is the assertion that the variables being tested are not related and the results are the product of random chance events. Remember that null is kind of like no so a null hypothesis means there is no relationship.
For example, if a researcher asks the question "Does having class for 12 hours in one day lead to nursing student burnout?"
The hypothesis would indicate the researcher's best guess of the results: "A 12 hour day of classes causes nursing students to burn out."
Therefore the null hypothesis would be that "12 hours of class in one day has nothing to do with student burnout."
The only way of backing up a hypothesis is to refute the null hypothesis. Instead of trying to prove the hypothesis that 12 hours of class causes burnout the researcher must show that the null hypothesis is likely to be wrong. This rule means assuming that there is not relationship until there is evidence to the contrary.
In every study there is a chance for error. There are two major types of error in quantitative research -- type 1 and 2. Logically, since they are defined as errors, both types of error focus on mistakes the researcher may make. Sometimes talking about type 1 and type 2 errors can be mentally tricky because it seems like you are talking in double and even triple negatives. It is because both type 1 and 2 errors are defined according to the researcher's decision regarding the null hypothesis, which assumes no relationship among variables.
Instead of remembering the entire definition of each type of error just remember which type has to do with rejecting and which one is about accepting the null hypothesis.
A type I error occurs when the researcher mistakenly rejects the null hypothesis. If the null hypothesis is rejected it means that the researcher has found a relationship among variables. So a type I error happens when there is no relationship but the researcher finds one.
A type II error is the opposite. A type II error occurs when the researcher mistakenly accepts the null hypothesis. If the null hypothesis is accepted it means that the researcher has not found a relationship among variables. So a type II error happens when there is a relationship but the researcher does not find it.
To remember the difference between these errors think about a stubborn person. Remember that your first instinct as a researcher may be to reject the null hypothesis because you want your prediction of an existing relationship to be correct. If you decide that your hypothesis is right when you are actually wrong a type I error has occurred.
A type II error happens when you decide your prediction is wrong when you are actually right.
One way to help you remember the meaning of type 1 and 2 error is to find an example or analogy that helps you remember. As a nurse you may identify most with the idea of thinking about medical tests. A lot of teachers use the analogy of a court room when explaining type 1 and 2 errors. I thought students may appreciate our example study analogy regarding class schedules.
It is impossible to know for sure when an error occurs, but researchers can control the likelihood of making an error in statistical decision making. The likelihood of making an error is related to statistical considerations that are used to determine the needed sample size for a study.
When determining a sample size researchers need to consider the desired Power, expected Effect Size and the acceptable Significance level.
Power is the probability that the researcher will make a correct decision to reject the null hypothesis when it is in reality false, therefore, avoiding a type II error. It refers to the probability that your test will find a statistically significant difference when such a difference actually exists. Another way to think about it is the ability of a test to detect an effect if the effect really exists.
The more power a study has the lower the risk of a type II error is. If power is low the risk of a type II error is high. ...

Views: 92424
NurseKillam

Cucudata Short Videos: SPSS Data Analysis Tutorial
Please Visit：www.cucudata.com
For More Videos and Files
Download More Data and Information
Ask Experts & Get Answers
Our Professional Team Provides a Comprehensive Data Analysis & Data Mining Services

Views: 35
spss

SPSS provides a correction to the t-test in cases where there are unequal variances. However, when one has unequal variances and unequal sample sizes, this correction is no longer accurate. In this video, I demonstrate a simple approach to dealing with the problem of unequal variances and sample sizes.

Views: 48991
how2stats

Visit https://skills.presentationexpressions.com/presentations for the Elite Presentation Skills Course.
In this video, you'll learn how to give a short, complete presentation. I also analyse the presentation and explain what happens at each stage of the presentation. I've put the analysis on my website to keep the video short. Click the following link to read the analysis: http://wp.me/pJrVJ-vq
Thanks!
Carl Kwan
Presentations | Video | Marketing - Creating Stories That Sell
http://www.carlkwan.com
PRESENTATION EXPRESSIONS
English presentation skills and tips
http://presentationexpressions.com
LinkedIn
http://kr.linkedin.com/in/carlkwan
Twitter
http://twitter.com/carlkwan

Views: 110619
Carl Kwan

In this video, we examine hypothesis tests assuming small sample size (under 30).

Views: 36
Gregory Fulkerson

Basic Statistical Tests
Training session with Dr Helen Brown, Senior Statistician, at The Roslin Institute, December 2015.
************************************************
These training sessions were given to staff and research students at the Roslin Institute. The material is also used for the Animal Biosciences MSc course taught at the Institute.
************************************************
*Recommended YouTube playback settings for the best viewing experience: 1080p HD
************************************************
Content:
‘Continuous’ data
-Measurements recorded on a scale
-Eg :
--White blood cell count
--Blood pressure
--Temperature
-2 types of test :-
--‘Parametric’ tests: suitable for normally distributed data
--‘Non-parametric’ tests: suitable for any continuous data, based on ranks of the data values
Analyses for data assumed to have a normal distribution
Checking normality
-Simple check can be based on histogram
-Satisfactory if roughly symmetrical
-Ideally data should be normal within each group tested
--In practice satisfactory if histogram for full data is symmetrical
--If full histogram has several modes, consider histograms for groups separately
Checking normality – smaller samples
-Histogram may not be smooth even if data are normal
-Difficult to determine whether normal
-Information on same measurements from previous larger studies may be helpful
-Sometimes still clear that data are non-normal, even in small sample
Data not normal?
-First consider a transformation of the data, particularly if the plots reveal a pattern
-If non-normality is due to outliers, can their deletion be justified?
-If normality in doubt :

Views: 20617
The Roslin Institute - Training

With Spanish subtitles. This video explains how to use the p-value to draw conclusions from statistical output. It includes the story of Helen, making sure that the choconutties she sells have sufficient peanuts.
You might like to read my blog:
http://learnandteachstatistics.wordpress.com

Views: 791241
Dr Nic's Maths and Stats

“From Statistical Inference to Probabilistic Reasoning; Using Bayesian Networks for Reasoning with Small Samples, Missing Values, and No Data.” Stefan Conrady, Managing Partner, Bayesia USA.
PSC Hot Topics 2016 Workshop: Small-Sample-Size Statistics in Agriculture; How to Maximize Business Value, November 3, 2016 Knoxville, TN

Views: 55
Phenotype Screening Corporation

Cucudata Short Videos: SPSS Data Analysis Tutorial
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spss

This video demonstrates a few ways to analyze pretest/posttest data using SPSS.

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Dr. Todd Grande

Download files: http://people.highline.edu/mgirvin/excelisfun.htm
Topics in this video:
1. (00:12) What is the t Distribution and when do we have to use it? Create Confidence Intervals when Population Standard Deviation is not known
2. (04:13) Formulas for t distribution to build Confidence Intervals when Population Standard Deviation is not known. Math formulas and Excel functions
3. (05:21) Example 1: Printer Manufacturer Example to create Confidence Interval
4. (06:41) Printer Manufacturer Example: Calculate Sample Mean, Sample Standard Deviation, Sample Size, Degrees of Freedom, Standard Error, Alpha and Alpha/2
5. (08:16) Printer Manufacturer Example: T.INV function to calculate t
6. (09:24)Printer Manufacturer Example: CONFIDENCE.T function to calculate Margin of Error
7. (11:38)Printer Manufacturer Example: Data Analysis Descriptive Statistics, Summary Statistics, Confidence Interval for Mean
8. (14:57) Restaurant Rating Example: All 3 Methods: 1) T.INV function, 2) CONFIDENCE.T function, 3) Data Analysis Descriptive Statistics

Views: 6438
ExcelIsFun

Until now, Stata provided only large-sample inference based on normal and χ² distributions for linear mixed-effects models. In small samples, the sampling distributions of test statistics are known to be t and F in simple cases, and those distributions can be good approximations in other cases.
Stata 14 provides five methods for small-sample inference, including Satterthwaite and Kenward-Roger. In this short video, we show you how to specify a small-sample adjustment method and new adjustments for postestimation hypothesis testing.
For more information about Stata's new small-sample inference capabilities, you can also check out
http://stata.com/stata14/small-sample-mixed-models/ Copyright 2011-2017 StataCorp LLC. All rights reserved.

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StataCorp LLC

When collecting data to make observations about the world it usually just isn't possible to collect ALL THE DATA. So instead of asking every single person about student loan debt for instance we take a sample of the population, and then use the shape of our samples to make inferences about the true underlying distribution our data. It turns out we can learn a lot about how something occurs, even if we don't know the underlying process that causes it. Today, we’ll also introduce the normal (or bell) curve and talk about how we can learn some really useful things from a sample's shape - like if an exam was particularly difficult, how often old faithful erupts, or if there are two types of runners that participate in marathons!
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CrashCourse

Statgraphics: Sample Size Determination Webinar - This webinar describes the Statgraphics procedures that can assist users in determining suitable sample sizes for common statistical analyses. Applications covered include estimation of population parameters, comparison of 2 or more samples, statistical tolerance limits, capability indices, control charts, screening experiments, and acceptance sampling.
To access the slide presentation PDF and/or associated data files, please visit: http://www.statgraphics.com/webinars.htm

Views: 947
Statgraphics

Bootstrap is very simple technique used for small samples. Major takeaways from video are:
What?: Resampling with replacement from sample data.
Why?: To find std errors without invoking CLT.
How?: K times repetitive sampling of size n (observation).
When to use?: Most suitable for small sample sizes.
Major Advantage: No need to invoke CLT or normality assumption

Views: 17814
Sarveshwar Inani

I demonstrate how to perform and interpret a paired samples t-test in SPSS. I also point out that many people fail to test the homogeneity of variance assumption in the paired samples t-test, but that this can be done relatively easily with a Pitman-Morgan test.
paired t-test

Views: 296791
how2stats

a short tutorial to introduce students to some terms involved in experimental uncertainty

Views: 274
Nicole Carro

Multilevel models can be fit even when the number of clusters is relatively low. Small-sample techniques for conducting estimation and inference produce valid results with as few as 10 clusters...
For a great review of this topic, see McNeish & Stapleton (2016, Educational Psychology Review) at https://www.researchgate.net/publication/269336961_The_Effect_of_Small_Sample_Size_on_Two_Level_Model_Estimates_A_Review_and_Illustration
Note: The posted video is an expanded remake of an earlier video on this topic.

Views: 1435
Curran-Bauer Analytics

Scientists studying biomarkers associated with human diseases and drug responses commonly use targeted immunoassays for marker detection and quantitation. Challenges associated with conventional detection methods such as Western blotting and ELISAs include limitations imposed by small sample volume due to use of rodent models and the need for greater sensitivity than conventional test formats.
The introduction of multiplex xMAP® technology (Luminex) that combines advanced fluidics, optics, and digital signal processing with microsphere technology has enabled multiplexed assay capabilities and enables acquisition of more data in less time than other bioassay formats, with comparable results to ELISA. Featuring a flexible, open-architecture design, xMAP can be configured to perform a wide variety of bioassays quickly, cost-effectively, and accurately.
Recently, for example, scientists have applied xMAP technology to detection and expression of cytokine expression and secretion in lymphocyte samples from mouse and human sources, detecting over 20 different cytokines in samples smaller than 50 uL. This technology has also allowed assessment of cytokine expression in human diseases, before and after immunization and in the healthy general population, as well as the study of various mouse models of autoimmune diseases.
This multiplexed cytokine assay allows generation of large amounts of data and therefore new analyses, which result in new insights into the roles of cytokines in health and disease.
In this webinar, investigators will describe how Thermo Fisher Scientific’s Cytokine Human 30-Plex Panel for the Luminex platform, was used to demonstrate pharmacological inhibition of G protein-coupled estrogen receptor (GPER) as measured by a decrease in pro-inflammatory cytokines. Scientists will also describe how ongoing multiplex assay development for cancer-specific cytokine and autoantibody immunoassays may enable differentiation of lung cancer subtypes in patients.

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GENNews

With Geogebra, Patrick walks through a Hypothesis Test with a small sample!

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SD Solutions LLC

http://www.stomponstep1.com/cohort-case-control-meta-analysis-cross-sectional-study-designs/
Based on the types of bias that are inherent in some study designs we can rank different study designs based on their validity. The types of research studies at the top of the list have the highest validity while those at the bottom have lower validity. In most cases if 2 studies on the same topic come to different conclusions, you assume the trial of the more valid type is correct. However, this is not always the case. Any study design can have bias. A very well designed and executed cohort study can yield more valid results than a clinical trial with clear deficiencies.
• Meta-analysis of multiple Randomized Trials (Highest Validity)
• Randomized Trial
• Prospective Cohort Studies
• Case Control Studies or Retrospective Cohort
• Case Series (Lowest Validity)
Meta-analysis is the process of taking results from multiple different studies and combining them to reach a single conclusion. Doing this is sort of like having one huge study with a very large sample size and therefore meta-analysis has higher power than individual studies.
Clinical trials are the gold standard of research for therapeutic and preventative interventions. The researchers have a high level of control over most factors. This allows for randomization and blinding which aren't possible in many other study types. Participant's groups are assigned by the researcher in clinical trials while in observational studies "natural conditions" (personal preference, genetics, social determinants, environment, lifestyle ...) assign the group. As we will see later, the incidence in different groups is compared using Relative Risk (RR).
Cohort Studies are studies where you first determine whether or not a person has had an exposure and then you monitor the occurrence of health outcomes overtime. It is the observational study design with the highest validity. Cohort is just a fancy name for a group, and this should help you remember this study design. You start with a group of people (some of whom happen to have an exposure and some who don't). Then you follow this group for a certain amount of time and monitor how often certain diseases or health outcomes arise. It is easier to conceptually understand cohort studies that are prospective. However, there are retrospective cohort studies also. In this scenario you identify a group of people in the past. You then first identify whether or not these people had the particular exposure at that point in time and determine whether or not they ended up getting the health outcomes later on. As we will see later, the incidence in different groups in a cohort study is compared using Relative Risk (RR).
Case-Control Studies are retrospective and observational. You first identify people who have the health outcome of interest. Then you carefully select a group of controls that are very similar to your diseased population except they don't have that particular disease. Then you try to determine whether or not the participants from each group had a particular exposure in the past. I remember this by thinking that in a case control study you start off knowing whether a person is diseased (a case) or not diseased (a control). There isn't a huge difference between retrospective cohort and case-control. You are basically doing the same steps but in a slightly different order. However, the two study designs are used in different settings. As we will see later, the incidence in different groups in a case-control study is compared using Odds Ratio (OR).
A Case-Series is a small collection of individual cases. It is an observational study with a very small sample size and no control group. Basically you are just reviewing the medical records for a few people with a particular exposure or disease. A study like this is good for very rare exposures or diseases. Obviously the small sample size and lack of a control group limits the validity of any conclusions that are made, but in certain situations this is the best evidence that is available.
Cross Sectional Studies are different from the others we have discussed. While the other studies measure the incidence of a particular health outcome over time, a cross-sectional study measures Prevalence. In this observational study the prevalence of the exposure and the health outcome are measured at the same time. You are basically trying to figure out how many people in the population have the disease and how many people have the exposure at one point in time. It is hard to determine an association between the exposure and disease just from this information, but you can still learn things from these studies. If the exposure and disease are both common in a particular population it may be worth investing more resources to do a different type of study to determine whether or not there is a causal relationship.

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Stomp On Step 1

See an how to calculate Sample Size for Confidence Intervals.
Busn 210 Business Statistical Using Excel Highline Community College taught by Mike Gel excelisfun Girvin

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ExcelIsFun

Description

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MATHGODDESSKERN KERN AP STATISTICS

“Doctor, It Hurts When I p”, Ronald L. Wasserstein, Executive Director, American Statistical Association
PSC Hot Topics 2016 Workshop: Small-Sample-Size Statistics in Agriculture; How to Maximize Business Value, November 3, 2016 Knoxville, TN

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Phenotype Screening Corporation

Unpaired t-test Hypothesis test using Excel (Small sample). Go to www.allegany.edu/math and click on Excel sheets

Views: 3577
Mark Shore

A 3-minute tutorial that demonstrates how to generate a random sampling of records using Excel.

Views: 309175
Timothy DAuria