Statistical hypothesis testing

Two-sample hypothesis testing

In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. There are a large number of statistical tests that can be used in a two-sample test. Which one(s) are appropriate depend on a variety of factors, such as: * Which assumptions (if any) may be made a priori about the distributions from which the data have been sampled? For example, in many situations it may be assumed that the underlying distributions are normal distributions. In other cases the data are categorical, coming from a discrete distribution over a nominal scale, such as which entry was selected from a menu. * Does the hypothesis being tested apply to the distributions as a whole, or just some population parameter, for example the mean or the variance? * Is the hypothesis being tested merely that there is a difference in the relevant population characteristics (in which case a two-sided test may be indicated), or does it involve a specific bias ("A is better than B"), so that a one-sided test can be used? (Wikipedia).

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Hypothesis Test: Two Population Proportions

This video explains how to conduct a hypothesis test on two population proportions. http://mathispower4u.com

From playlist Hypothesis Test with Two Samples

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Hypothesis Test: Two Population Means with Unknown Standard Deviations (2 Tailed)

This video explains how to conduct a hypothesis test with two population means with unknown standard deviations. http://mathispower4u.com

From playlist Hypothesis Test with Two Samples

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Hypothesis Test: Two Population Means with Unknown Standard Deviations (Data)

This video explains how to conduct a hypothesis test on two population means with unknown standard deviations. http://mathispower4u.com

From playlist Hypothesis Test with Two Samples

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2 Sample t Test v Paired t Test

Identifying the difference between situations when a 2-sample t Test is appropriate and when a paired t Test is appropriate, including the recognition of paired dependent data versus independent samples.

From playlist Unit 9: t Inference and 2-Sample Inference

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One Sample t Test Framework with Gardening Example (15-3)

The one sample z test is a parametric procedure that tests whether a single sample mean is significantly different than a population mean when the standard deviation of the population (σ) is UNKNOWN. We will learn the research design for the IV and DV, the assumptions for the test, how to

From playlist Single-Sample Hypothesis Tests (z, t, & p) - WK 15 QBA 237

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A Gentle Introduction to the One Sample t Test (10-2)

A one-sample t test will allow us to compare a sample mean to a population mean to determine if they are statistically significantly different. This video will introduce you to the fundamentals of the one-sample t test and prepare you for conducting one either by hand or using SPSS. This

From playlist WK10 One Sample t Tests - Online Statistics for the Flipped Classroom

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Hypothesis Test: Two Population Means with Unknown Standard Deviations (Right Tailed)

This video explains how to conduct a hypothesis test with two population means with unknown standard deviations. http://mathispower4u.com

From playlist Hypothesis Test with Two Samples

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One Sample z Test Framework with Social Media Example (15-2)

The one sample z test is a parametric procedure that tests whether a single sample mean is significantly different than a population mean when the standard deviation of the population (σ) is KNOWN. We will learn the research design for the IV and DV, the assumptions for the test, how to se

From playlist Single-Sample Hypothesis Tests (z, t, & p) - WK 15 QBA 237

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Two Tail Hypothesis Test for Difference in Proportions | Statistics

We perform a two-tailed two sample z test for a difference in population proportions. This two proportion hypothesis test will require us to state our null hypothesis and alternative hypothesis, then calculate the pooled proportion to use in our standard error calculation, and then finally

From playlist Statistics

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Hypothesis Testing Statistics | Hypothesis Testing in Data Science | Data Science Training | Edureka

🔥Edureka Data Science Master Program: https://www.edureka.co/masters-program/data-scientist-certification This Edureka video on "Hypothesis Testing Statistics” will provide you with a comprehensive and detailed understanding of concepts of Statistical Hypothesis Testing. Moreover, it also

From playlist Data Science Training Videos

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Hypothesis testing (ALL YOU NEED TO KNOW!)

0:00 Introduction 3:41 Intuition behind hypothesis testing 10:16 Example 1 12:57 Null hypothesis 22:00 Test statistic 28:27 p-valiue 33:38 Confidence intervals 37:46 Significant treatment difference 42:25 Power and Sample size (THE BEST!) 50:47 Example 2

From playlist Statistical Inference (7 videos)

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Analyze Phase In Six Sigma | Six Sigma Green Belt Training

The fourth lesson of the Lean Six Sigma Green Belt Course offered by Simplilearn. This lesson will cover the details of the analyze phase. In the Lean Six Sigma process, you begin with the define phase where you define the problem and then the current process performance is measured. Next

From playlist Six Sigma Training Videos [2022 Updated]

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Python for Data Analysis: Hypothesis Testing and T-Tests

This video covers the basics of statistical hypothesis testing and t-tests in Python. This video explains the basics of statistical hypothesis testing and shows how to run one-way, two-way and paired t-tests in Python. Subscribe: ► https://www.youtube.com/c/DataDaft?sub_confirmation=1 Th

From playlist Python for Data Analysis

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STAT 200 Lesson 5 Full Video Lecture

Table of Contents: 00:25 - Learning objectives 00:43 - Review: Symbols 01:59 - Hypothesis testing scenario example 02:58 - Five step hypotehsis testing procedure 04:43 - 1. Identify and write null and alternative hypotheses 08:26 - Example: Proportion broken products 10:15

From playlist STAT 200 Video Lectures

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Introduction to R: T-Tests (Hypothesis Testing)

This is lesson 24 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below: Intro to R: Hypothesis Testing https://www.kaggle.com/hamelg/intro-to-r-part-24-Hypothesis-Testing This lesson covers statistical hypothes

From playlist Introduction to R

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STAT 200 Lesson 6 Full Lecture Video

***There is an error in the formula for the pooled standard deviation in this video from 17:17 to 27:53. The denominator should be n1+n2-2. For the correct formula and an example, see https://online.stat.psu.edu/stat200/lesson/6/6.4

From playlist STAT 200 Video Lectures

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STAT 200 Lesson 9 Lecture

Table of Contents: 00:50 - Lecture structure Two Proportions 01:11 - Checking assumptions 02:50 - Computing the standard error by hand 03:59 - Example: Computing the standard error for a confidence interval 06:22 - Example: Computing the standard error for a hypothesis test 08

From playlist STAT 200 Video Lectures

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Statistical data analysis | Statistical Data Science | Part 1

In this course you will learn how to analyze data. #Statistic plays important role in terms of data analysis. Here you will get exposed to utilize and understand various statistical method to analyse data. The following topic has discussed in this course. - Central tendency (mean and me

From playlist Data Analysis

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Null and Alternative Hypotheses in Statistics (7-8)

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

From playlist WK7 Sampling, Probability, & Inference - Online Statistics for the Flipped Classroom

Related pages

Kernel embedding of distributions | Tukey–Duckworth test | Variance | Student's t-test | Welch's t-test | Kolmogorov–Smirnov test | Alternative hypothesis | Kuiper's test | Paired data | Hotelling's T-squared distribution | Nominal scale | Mean | Pearson's chi-squared test | Normal distribution | Statistical hypothesis testing | Median test