Video lesson for ALEKS statistics

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John Wood

Get the full course at: http://www.MathTutorDVD.com
The student will learn how to write the null and alternate hypothesis as part of a hypothesis test in statistics. We will work several examples so that the student gains an understanding of how to work hypothesis testing problems step-by-step.

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mathtutordvd

The simplest and most effective rule to correctly setting up the null and alternate hypotheses. Follow this step-by-step process and you will never get it wrong!
This is a 7 minute lecture on the fool-proof rules to setting up the null and alternate hypotheses in Statistics.
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Dave Your Tutor

Examples of how to write null and alternative hypotheses.
View more lessons or practice this subject at http://www.khanacademy.org/math/ap-statistics/tests-significance-ap/idea-significance-tests/v/examples-of-null-and-alternative-hypotheses?utm_source=youtube&utm_medium=desc&utm_campaign=apstatistics
AP Statistics on Khan Academy: Meet one of our writers for AP¨_ Statistics, Jeff. A former high school teacher for 10 years in Kalamazoo, Michigan, Jeff taught Algebra 1, Geometry, Algebra 2, Introductory Statistics, and AP¨_ Statistics. Today he's hard at work creating new exercises and articles for AP¨_ Statistics.
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Khan Academy

Some basic tips on how to write null and alternative hypotheses for hypothesis testing.

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Steve Mays

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Clutch Prep = Textbook specific videos to help you pass your toughest science classes.

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Clutch Prep

How to state the null hypothesis in easy steps.

Views: 65957
Stephanie Glen

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

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statslectures

In this video we examine hypothesis tests, including the null and alternative hypotheses. We take a look at a few different examples, with a focus on two-tailed tests in this video.
Null hypothesis
Alternative hypothesis
H0
H1
Video Transcript: In this video we'll take a look at null and alternative hypotheses. Now the null hypothesis means there's no effect, or nothing happened, or there's no difference. The null hypothesis is often represented by H Sub zero. And it makes a statement about the population, not the sample. So in other words we put population values or symbols in our null hypothesis. Now the alternative hypothesis is really the opposite. It states or it means that there was an effect, or something happened, or there was a difference. The alternative hypothesis is often represented by H sub 1 or H sub A, and it also makes a statement about the population, not the sample. So if we take a look at these two side-by-side, once again, in review, the null is stated by H sub 0 the alternative is H sub 1 or H sub A. The null basically states nothing happened, and look at the opposite here, the alternative states something happened. Or the null can state no effect, the alternative states there was an effect. And, finally, the null can state no difference effectively, and the alternative would state the opposite, there was a difference. And once again both hypotheses refer to the population. Let's go ahead and take a look at an example using the Pearson correlation or Pearson's r. Now correlation measures the degree of the linear relationship, if there's any at all, between two variables, and it's known as Pearson's r. Let's go and take a look at the null and alternative hypotheses for correlation, or fir Pearson's r here. In words the null would state there is not a relationship between the two variables in the population. The alternative would state the opposite: it would state there is a relationship between the two variables in the population. Notice how the null states no effect, or there's no relationship, whereas the alternative states there is an effect, or there is a relationship. Using symbols we could say the following: the null, and that little thing that looks like a p there, that stands for rho, and it's the correlation in the population. So we would say null rho x,y equals 0 and then the alternative would say rho x,y does not equal zero. Or, in other words, the null would state there's no correlation between x and y, two variables in the population, whereas the alternative would state there is a correlation between the two variables, x and y, in the population. And 0 here means no relationship in correlation. So when the null says it's equal to 0, it's saying there's no relationship. When the alternative says it's not equal to 0, it stating there is a relationship. So, in review, the null states there's no effect or zero relationship, whereas the alternative states there is an effect, or a non-zero relationship. Now hypotheses need to be mutually exclusive and exhaustive. Exclusive means there's no overlap between the null and the alternative. And if you look at our two statements up above, where it says rho x,y equals 0, and rho x,y does not equal zero, notice that those do not overlap at all, equals and not equals are completely non overlapping. It's either 0, which is the null in that case, or it's not zero, which is the alternative. So they're completely exclusive, they do not overlap. And then exhaustive means they must cover, or exhaust, all possibilities, the null and alternative when taken together. And notice that they do, as every possible value for Pearson's r is either 0 or not 0, so it does exhaust all possibilities. So once again it’s exclusive, because they don't overlap, and it's exhaustive, because they cover all possibilities. Now notice how the alternative has a not equal sign, implying that the alternative hypothesis can be either greater than zero, or less than zero, or in other words correlation can be positive or negative. This is known as a two-tailed test, since the alternative hypothesis consists of two possibilities, either greater than zero or less than zero. Alternatively, one-tailed tests can also be used in hypothesis testing, and we'll examine one-tailed tests in another video.

Views: 39294
Quantitative Specialists

In this video we see 6 example of writing null and alternative hypothesis including equal to, less than, greater than, is at least, is no more than, and more than half.

Views: 109
Emporium Mathematics

This video demonstrates how to determine the null and alternative hypothesis, id & dd, 1 or 2 tailed test.

Views: 128
Moore Statistics

How to state the null hypothesis and the alternative hypothesis, and how to tell the type of test

Views: 1799
Nickie Christensen

There are four steps in data-driven decision-making.
First, you must formulate a hypothesis.
Second, once you have formulated a hypothesis, you will have to find the right test for your hypothesis.
Third, you execute the test.
And fourth, you make a decision based on the result.
Let’s start from the beginning. What is a hypothesis? Though there are many ways to define it, the most intuitive I’ve seen is:
“A hypothesis is an idea that can be tested.”
This is not the formal definition, but it explains the point very well.
So, if I tell you that apples in New York are expensive, this is an idea, or a statement, but is not testable, until I have something to compare it with. For instance, if I define expensive as: any price higher than $1.75 dollars per pound, then it immediately becomes a hypothesis.
Alright, what’s something that cannot be a hypothesis? An example may be: would the USA do better or worse under a Clinton administration, compared to a Trump administration? Statistically speaking, this is an idea, but there is no data to test it, therefore it cannot be a hypothesis of a statistical test. Actually, it is more likely to be a topic of another discipline. Conversely, in statistics, we may compare different US presidencies that have already been completed, such as the Obama administration and the Bush administration, as we have data on both.
Generally, the researcher is trying to reject the null hypothesis. Think about the null hypothesis as the status quo and the alternative as the change or innovation that challenges that status quo. In our example, Paul was representing the status quo, which we were challenging.
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Views: 27930
365 Data Science

Views: 65513
MrNystrom

Demonstrates the basics of hypothesis testing using the P-value method: find the test statistic which in turn gives us the P-value, then compare the P-value to the level of significance (alpha) to determine whether or not the Null Hypothesis is rejected or not.
PLEASE READ!! I did not make a mistake by showing .0045 as the area (probability) for 2.61. Look carefully at my picture. I have shaded to the **RIGHT** of 2.61, not to the left. If you look up 2.61 on your table and see .9955 your table is assuming you want the area shaded to the LEFT of 2.61. Again, I shaded to the RIGHT of 2.61. I hope that makes sense!

Views: 1048805
poysermath

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
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Missed the previous lesson?
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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!
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Khan Academy

Get this complete course at http://www.MathTutorDVD.com
In this lesson, we will discuss the very important topic of p-values in statistics. The p-value is a calculation that we make during hypothesis testing to determine if we reject the null hypothesis or fail to reject it.
The p-value is calculated by first finding the z test statistic. Once this is known we then need to find the probability of our population having a value more extreme than the test statistic. This is done by looking up the probability in a normal distribution table. We then interpret the results by comparing the p-value to the level of significance.
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Views: 496107
mathtutordvd

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ProfLMurray

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: 1319454
mathtutordvd

Hypothesis Testing: Null and Alternative Hypothesis (one sample t test)!
More Statistics & R Programming Videos: https://goo.gl/4vDQzT
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In the second video in Statistics 101 Hypothesis Testing series, we will learn about the null and alternative hypotheses and how they are set up in a hypothesis test in the context of a one-sample t-test.
►Hypothesis testing works by first specifying a Null Hypothesis and an Alternative Hypothesis. The Null hypothesis is generally a statement of “no change” or “nothing interesting happening here”, while the Alternative hypothesis is generally what we are interested in “proving”, or more correctly, what we are interested in “providing evidence for”. Hypothesis tests generally start by assuming the Null hypothesis to be true, and then proceed to work out how likely we would have been to observed what we did in our sample (the sample statistic, or sample mean in this case) if the Null hypothesis were in fact true. This “how likely we were to observe it” is known as a p value.
►The One Sample t Test helps us decide whether or not we believe the sample mean is statistically different from a known or hypothesized population mean. The One Sample t Test is a parametric test. This test is also known as: Single Sample t Test or Student’s t test or when one assumes the population standard deviation is known, it is called the Z test. The test works by calculating a test statistic that measures how compatible the sample statistic (the sample mean in this case) is with the value that would be expected for the sample statistic if the null hypothesis were in fact true (the null hypothesized value). A p value is then calculated. The p value tells us the probability of obtaining a sample statistic as far, or further, from the null hypothesized value, if the null hypothesis were in fact true. The p value helps us decide whether or not we will believe the null hypothesis to be true or not.
In this set of tutorials (Statistics 101), you will learn the concept of a null and alternative hypothesis, how a test statistic can be used to measure the compatibility of our data with the null hypothesis, the use of a significance level and p-values or critical regions, conclusions that we can make as well as the errors that may be made when drawing our conclusions.
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MarinStatsLectures- R Programming & Statistics

Visit http://www.statisticshowto.com for more information on the null hypothesis.

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Stephanie Glen

SKIP AHEAD:
0:39 – Null Hypothesis Definition
1:42 – Alternative Hypothesis Definition
3:12 – Type 1 Error (Type I Error)
4:16 – Type 2 Error (Type II Error)
4:43 – Power and beta
6:33 – p-Value
8:39 – Alpha and statistical significance
14:15 – Statistical hypothesis testing (t-test, ANOVA & Chi Squared)
For the text of this video click here http://www.stomponstep1.com/p-value-null-hypothesis-type-1-error-statistical-significance/
For my video on Confidence Intervals click here http://www.stomponstep1.com/confidence-interval-interpretation-95-confidence-interval-90-99/

Views: 425275
Stomp On Step 1

To use data and statistics properly, it's important to understand hypotheses. In particular, it's important to understand the difference between null and research hypotheses. Knowing more about hypotheses as I describe here will help you better understand the concept of null hypothesis significance testing, or the overall topic of statistical significance.

Views: 28221
Ben Baran

How is Null hypothesis different from Alternative Hypothesis?
An explanation with a simple example " Effect of fertilizer 'x' on plant growth" (Less than 5 min video)
This video include
-What is hypothesis?
-Definition of Null hypothesis and Alternative Hypothesis.
-Summary of Scientific method
-Difference between independent and dependant variable in an experiment.
For notes: http://www.majordifferences.com/2016/10/5-differences-between-null-and.html

Views: 69414
biologyexams4u

There are two types of hypotheses we need for hypothesis testing: the one we test (the null hypothesis) and the one that use instead if we reject the null hypothesis (the alternative hypothesis). The null hypothesis (H0) states there is no difference between the experimental (sample) mean and the control (population) mean; therefore, any differences between the experimental mean and the control (population) mean are due to chance. The null hypothesis is also a falsifiable hypothesis.
Table of Contents:
00:24 - Null Hypothesis
01:36 - Alternative Hypothesis

Views: 7654
Research By Design

Check out http://cie.000webhostapp.com/ for more.

Views: 419
Free Lessons for CIE A-level Maths 9709

This video provides a examples of null and alternative hypotheses in hypothesis testing. Learn how to correctly state the null and alternative hypothesis in statistics.

Views: 5164
Quantitative Specialists

How to state the null and alternative hypothesis, & identify which is the claim

Views: 770
tboddy19

This lesson on inferential statistics shows you how to do the hypothesis testing procedure. From writing out the 2 competing hypotheses to determining if you are rejecting or not rejecting the null hypothesis.
http://www.numberbender.com/ Lesson on how to write null and alternative hypotheses in inferential statistics
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Views: 8349
Numberbender

The lecture builds up on Significance Testing based on previous lectures. We demonstrate hypothesis formulating, setting a alpha level to test the hypothesis against. Single sample test of means are discussed in the lecture alongwith acceptance and rejection regions around the hypothesized means.
The lecture demonstrates using an example where we claim a certain hypothesis regarding the mean of the population. Using CLT – central limit theorem properties, we test the claim using sample mean and sample standard deviation.
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The objective of the training series is to prepare the student for a career in Data Analysis and the Analytics Industry in general. Please visit our website for further details.
If you wish to subscribe to our full Analytics Training module, please visit http://goo.gl/nIJJHg for our paid Youtube Channel which contains additional hours covering more extensive topics including Linear Regression/Logistic Regression, building and testing predictive models using Logistic / Decision Trees and Ensembling. All videos on our paid channel are available without advertisement interruptions and you can enrol for a 14 day free subscription trial. You pay only if you want to continue.
Additionally, you get access to the datasets discussed in the videos and all SAS Codes.

Views: 5036
Learn Analytics

An introduction to some of the terminology used in hypothesis testing.

Views: 58525
MathHolt

Step 2 in the five steps of hypothesis testing is to create a null and alternative hypothesis. The null hypothesis states that there is no difference between the sample mean and the population mean, so any differences are due to chance. The alternative hypothesis states that the sample mean and the population mean are truly different, so any differences are real and are due to a treatment effect. Alternative hypotheses can be either directional or non-directional. Directional refers to the direction that the dependent variable changes. We establish hypotheses for our baby weight example.
Course files & Bear Handout: https://drive.google.com/drive/folders/1n9aCsq5j4dQ6m_sv62ohDI69aol3rW6Q?usp=sharing
Table of Contents:
02:12 - Step 2: Null and Alternative Hypothesis
02:23 - Step 2: Null and Alternative Hypothesis
04:24 - Step 2: Null and Alternative Hypothesis
05:11 -

Views: 1664
Research By Design

This is just a few minutes of a complete course.
Get full lessons & more subjects at: http://www.MathTutorDVD.com.

Views: 2944
mathtutordvd

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5 Minutes Engineering

A lesson on how to easily write the null and alternative hypothesis.
Written for MTH 221 students using TIY #1 page 258

Views: 780
Beth Dodson

Introduction to hypothesis testing. The null and alternate hypotheses.

Views: 26
Jeff Suzuki

I have made a new and improved version of this video to correct a mistake. To be take to the new video please click here https://www.youtube.com/watch?v=YSwmpAmLV2s

Views: 90483
Stomp On Step 1

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

Views: 316310
statslectures

Excel file: https://dl.dropboxusercontent.com/u/561402/TTEST.xls
In this video Paul Andersen explains how to run the student's t-test on a set of data. He starts by explaining conceptually how a t-value can be used to determine the statistical difference between two samples. He then shows you how to use a t-test to test the null hypothesis. He finally gives you a separate data set that can be used to practice running the test.
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All of the images are licensed under creative commons and public domain licensing:
1.3.6.7.2. Critical Values of the Student’s-t Distribution. (n.d.). Retrieved April 12, 2016, from http://www.itl.nist.gov/div898/handbook/eda/section3/eda3672.htm
File:Hordeum-barley.jpg - Wikimedia Commons. (n.d.). Retrieved April 11, 2016, from https://commons.wikimedia.org/wiki/File:Hordeum-barley.jpg
Keinänen, S. (2005). English: Guinness for strenght. Retrieved from https://commons.wikimedia.org/wiki/File:Guinness.jpg
Kirton, L. (2007). English: Footpath through barley field. A well defined and well used footpath through the fields at Nuthall. Retrieved from https://commons.wikimedia.org/wiki/File:Footpath_through_barley_field_-_geograph.org.uk_-_451384.jpg
pl.wikipedia, U. W. on. ([object HTMLTableCellElement]). English: William Sealy Gosset, known as “Student”, British statistician. Picture taken in 1908. Retrieved from https://commons.wikimedia.org/wiki/File:William_Sealy_Gosset.jpg
The T-Test. (n.d.). Retrieved April 12, 2016, from http://www.socialresearchmethods.net/kb/stat_t.php

Views: 494713
Bozeman Science