Enterprise Database Systems
A Deep Dive into Statistical and Hypothesis Tests
Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing
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Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests
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Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis
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Statistical & Hypothesis Tests: Using the One-sample T-test
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Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing
Lesson Objectives
Statistical & Hypothesis Tests: Getting Started with Hypothesis Testing
- discover the key concepts covered in this course
- outline how descriptive and inferential statistics work
- describe the fundamentals of hypothesis testing
- set up null and alternative hypotheses for statistical tests
- interpret p-values using alpha levels
- explore the one-sample, two-sample, and paired-sample T-tests
- compare and contrast type I and type II errors in hypothesis testing
- apply the ANOVA test for multiple groups
- summarize the key concepts covered in this course
Overview/Description
Hypothesis testing is the bedrock of inferential statistics, allowing us to draw inferences reliably about the population as a whole. Use this course to learn more about the distinction between descriptive and inferential statistics and how the latter seek to generalize from the sample to the population as a whole. Examine the components of a typical hypothesis test, such as the null and alternative hypothesis, the test statistic, and the p-value. You'll also explore type-I and type-II errors and the use cases and conceptual underpinnings of t-tests and ANOVA. By the time you finish this course, you will be able to identify use-cases for hypothesis testing and conceptually construct the appropriate null and alternative hypotheses for such tests.
Hypothesis testing is the bedrock of inferential statistics, allowing us to draw inferences reliably about the population as a whole. Use this course to learn more about the distinction between descriptive and inferential statistics and how the latter seek to generalize from the sample to the population as a whole. Examine the components of a typical hypothesis test, such as the null and alternative hypothesis, the test statistic, and the p-value. You'll also explore type-I and type-II errors and the use cases and conceptual underpinnings of t-tests and ANOVA. By the time you finish this course, you will be able to identify use-cases for hypothesis testing and conceptually construct the appropriate null and alternative hypotheses for such tests.
Target
Prerequisites: none
Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests
Lesson Objectives
Statistical & Hypothesis Tests: Performing Two-sample T-tests & Paired T-tests
- discover the key concepts covered in this course
- recall the assumptions of the two-sample T-test
- use Levene’s test to check for equal variances
- use the two-sample T-test to compare means
- recognize when the Welch’s T-test should be used
- use the Welch’s T-test to compare means
- describe type I and type II errors
- outline the relationship between type I errors and alpha levels
- outline the relationship between type II errors and alpha levels
- set up and visualize data for the paired difference T-test
- perform the paired T-test on paired samples
- use the paired T-test to compare before and after values
- perform paired T-tests with varying outcomes for the null hypothesis
- summarize the key concepts covered in this course
Overview/Description
In situations where two independent samples are drawn from different populations or where paired samples are available, such as in a before-after scenario, two-sample and paired T-tests are needed, respectively. Use this course to explore how two-sample T-tests can be used to test the null hypothesis that two independent samples have drawn from populations with equal means. You'll examine type I and type II errors and the use of paired samples T-tests. By the time you finish this course, you will be able to test whether two samples - either drawn independently or explicitly linked - are drawn from populations with equal means.
In situations where two independent samples are drawn from different populations or where paired samples are available, such as in a before-after scenario, two-sample and paired T-tests are needed, respectively. Use this course to explore how two-sample T-tests can be used to test the null hypothesis that two independent samples have drawn from populations with equal means. You'll examine type I and type II errors and the use of paired samples T-tests. By the time you finish this course, you will be able to test whether two samples - either drawn independently or explicitly linked - are drawn from populations with equal means.
Target
Prerequisites: none
Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis
Lesson Objectives
Statistical & Hypothesis Tests: Using Non-parametric Tests & ANOVA Analysis
- discover the key concepts covered in this course
- recognize the use of the Mann-Whitney U-test
- use the Mann-Whitney U-test
- set up data for the paired Wilcoxon signed-rank test
- compare the paired T-test and the paired Wilcoxon signed-rank test
- identify the pairwise T-test for multiple categories
- use the pairwise T-test to test for different means
- outline the use of one-way ANOVA analysis
- outline one-way ANOVA and linear regression
- use Tukey’s HSD to know which categories differ significantly
- describe how ANOVA requires residuals to be normally distributed
- use the non-parametric Kruskal-Wallis test
- outline the use of the two-way ANOVA analysis
- use two-way ANOVA with interaction between the independent variables
- summarize the key concepts covered in this course
Overview/Description
Two-sample T-tests are great for comparing population means given two samples. However, if the number of samples increases beyond two, we need a much more versatile and powerful technique - analysis of variance (ANOVA). Use this course to learn more about non-parametric tests and the ANOVA analysis. In this course, you'll explore the different use cases for Mann-Whitney U-tests, the use of the non-parametric paired Wilcoxon signed-rank test, and perform pairwise T-tests and ANOVA. You'll also get a chance to try your hand at the non-parametric variant of ANOVA - Kruskal Wallis test and post hoc tests, such as Tukey’s honestly significant difference test (HSD). After completing this course, you will be able to account for the effect of one or two independent categorical variables, each having an arbitrary number of levels, on a dependent variable using ANOVA.
Two-sample T-tests are great for comparing population means given two samples. However, if the number of samples increases beyond two, we need a much more versatile and powerful technique - analysis of variance (ANOVA). Use this course to learn more about non-parametric tests and the ANOVA analysis. In this course, you'll explore the different use cases for Mann-Whitney U-tests, the use of the non-parametric paired Wilcoxon signed-rank test, and perform pairwise T-tests and ANOVA. You'll also get a chance to try your hand at the non-parametric variant of ANOVA - Kruskal Wallis test and post hoc tests, such as Tukey’s honestly significant difference test (HSD). After completing this course, you will be able to account for the effect of one or two independent categorical variables, each having an arbitrary number of levels, on a dependent variable using ANOVA.
Target
Prerequisites: none
Statistical & Hypothesis Tests: Using the One-sample T-test
Lesson Objectives
Statistical & Hypothesis Tests: Using the One-sample T-test
- discover the key concepts covered in this course
- install various modules in python
- create a function to manually perform a T-test
- compare a manual one-sample T-test to a built-in test
- explore Laplace and Wald distributions with T-tests
- test data to see if it is normally distributed using Shapiro-Wilk and Anderson-Darling tests
- perform T-tests on real-world data
- explore one-sided and two-sided T-tests
- perform the Wilcoxon signed-rank test to compare medians
- test medians using the Wilcoxon signed-rank test
- summarize the key concepts covered in this course
Overview/Description
One-sample T-tests are probably the single most commonly used type of hypothesis test. Through this course, learn to manually implement the one-sample T-test to know exactly how the p-value and test statistic are calculated. You'll examine various library implementations of the one-sample T-test and apply the test on data drawn from several different distributions. This course will also help you explore the non-parametric Wilcoxon signed-rank test, which is conceptually very similar to the one-sample T-test and helps estimate the median rather than the mean of that population without making assumptions about the population distribution. Upon completion of this course, you will be able to use the one-sample T-test as well as its non-parametric equivalent to evaluate both one-sided and two-sided hypotheses about the population mean or median.
One-sample T-tests are probably the single most commonly used type of hypothesis test. Through this course, learn to manually implement the one-sample T-test to know exactly how the p-value and test statistic are calculated. You'll examine various library implementations of the one-sample T-test and apply the test on data drawn from several different distributions. This course will also help you explore the non-parametric Wilcoxon signed-rank test, which is conceptually very similar to the one-sample T-test and helps estimate the median rather than the mean of that population without making assumptions about the population distribution. Upon completion of this course, you will be able to use the one-sample T-test as well as its non-parametric equivalent to evaluate both one-sided and two-sided hypotheses about the population mean or median.
Target
Prerequisites: none
