Intro to Null Hypothesis Significance Testing with z-test from Coursera

This is primarily aimed at first- and second-year undergraduates interested in psychology, data analysis, and quantitative research methods along with high school students and professionals with similar interests.

This course delves into the foundational concepts of probability and statistics, emphasizing the importance of random sampling and the normal distribution. Students will learn to apply statistical methods, including z-scores, effect size, and confidence intervals in the context of null hypothesis significance testing. The course also covers the implications of the central limit theorem and the relationship between statistical power and error types.

Table of Contents:

Probability and Distributions

The Normal Distribution

Sampling Distributions and the Central Limit Theorem

The Logic of Null Hypothesis Significance Testing

Null Hypothesis Significance Testing With the z-test

Errors in Null Hypothesis Significance Testing

Evaluating Statistical Significance

Effect Size, Confidence Intervals, and Power

What's inside

Syllabus

Learn With PsycLearn Essentials

This module introduces you to your PsycLearn Essentials course. Find out what’s included in this course and how to navigate the modules and lessons. You’ll also learn valuable study tips for successful learning.

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what should give you pause

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Introduces inferential statistics and hypothesis testing, which are essential for quantitative research in psychology and related fields

Covers z-tests, effect size, and confidence intervals, which are foundational statistical concepts for understanding research findings

Explores the implications of the central limit theorem, which is a cornerstone of statistical inference and hypothesis testing

Includes resources from the American Psychological Association, which may be valuable for students pursuing careers in psychology

Requires a basic understanding of probability, which may necessitate additional preparation for some learners

Reviews summary

Foundational intro to null hypothesis testing

According to learners, this course provides a clear and logical introduction to null hypothesis significance testing with the z-test. Many students find it an excellent starting point, particularly if they are new to statistics or require a strong foundation for psychology and research methods. While the focus on the underlying logic is appreciated by many, some reviewers note that it is primarily theoretical and may require supplemental practice. It is generally considered well-suited for beginners but potentially too basic for those with prior statistical knowledge.

Strong emphasis on logic over practical application.

"I appreciated the deep dive into the *why* of NHST, understanding the logic is key."

"The course focuses heavily on the theory, which is good, but I wish there were more practical 'how-to' examples."

"Excellent theoretical basis, but don't expect extensive hands-on data analysis practice here."

An excellent foundational course for new learners.

"This was the perfect course to start learning about statistical testing. It covered the basics thoroughly."

"Great for someone with zero background in statistics."

"I needed a solid introduction before tackling more advanced topics, and this delivered."

Highly relevant for psychology and research students.

"Essential for my psychology degree, directly applicable to research methods class."

"As a psychology student, this course felt tailor-made for understanding the stats I need."

"Helped me understand the statistical concepts used in psychological research papers."

Concepts are explained clearly, especially for beginners.

"The way the concepts were explained made Null Hypothesis Testing finally make sense to me."

"I found the explanations very clear and easy to follow, even without much prior stats knowledge."

"Complex ideas are broken down well, making them accessible for an introductory course."

May be too introductory for those with prior stats.

"If you've taken a stats course before, this might be mostly review."

"Was hoping for a bit more depth, it felt very basic at times."

"Good content, but it didn't build much on what I already knew from a college intro stats class."

Limited practice problems; supplemental work may be needed.

"Could benefit from more practice exercises to solidify understanding."

"I had to seek out extra problems online to feel confident applying the z-test."

"The course explains well, but there weren't enough opportunities to actually *do* the tests myself."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Intro to Null Hypothesis Significance Testing with z-test with these activities:

Review Basic Probability Concepts

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Reinforce your understanding of probability, which is crucial for grasping the concepts of sampling distributions and hypothesis testing covered in the course.

Browse courses on Probability

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Review definitions of probability, events, and sample spaces.
Work through practice problems involving calculating probabilities.
Familiarize yourself with different types of probability distributions.

Review 'Statistics for People Who (Think They) Hate Statistics'

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Solidify your understanding of statistical concepts with a book that is easy to understand.

View Tests & Measurement for People Who (Think They)... on Amazon

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Read the chapters related to probability and hypothesis testing.
Work through the examples and exercises provided in the book.
Relate the concepts in the book to the course material.

Calculate z-scores for practice datasets

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Improve your ability to calculate z-scores accurately and efficiently, which is essential for performing z-tests.

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Find or generate several datasets with sample and population means and standard deviations.
Calculate the z-score for each dataset using the appropriate formula.
Interpret the meaning of the calculated z-scores in the context of each dataset.

Four other activities

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Create a visual explanation of the Central Limit Theorem

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Deepen your understanding of the Central Limit Theorem by creating a visual aid that explains its key concepts and implications.

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Research and gather information about the Central Limit Theorem.
Choose a visual medium (e.g., infographic, animation, presentation).
Design and create the visual explanation, focusing on clarity and accuracy.
Share your visual explanation with peers for feedback.

Conduct peer review of hypothesis testing examples

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Improve your understanding of hypothesis testing by reviewing and critiquing examples with peers.

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Find a partner or small group of classmates.
Each person prepares a hypothesis testing example, including the null and alternative hypotheses, data, and conclusions.
Exchange examples and provide constructive feedback on each other's work.

Analyze a published research paper using z-tests

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Apply your knowledge of z-tests to analyze a real-world research paper and evaluate its statistical significance.

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Find a published research paper that uses z-tests or similar statistical methods.
Extract the relevant data and hypotheses from the paper.
Replicate the z-tests using the provided data.
Compare your results with the paper's conclusions and identify any discrepancies.

Review 'Statistical Power Analysis for the Behavioral Sciences'

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Expand your knowledge of statistical power and its impact on research outcomes.

View Statistical Power Analysis for the Behavioral... on Amazon

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Read the chapters related to power analysis and effect size.
Work through the examples and exercises provided in the book.
Apply the concepts in the book to the course material.

Career center

Learners who complete Intro to Null Hypothesis Significance Testing with z-test will develop knowledge and skills that may be useful to these careers:

Psychology Researcher

Psychology researchers design and conduct studies to understand human behavior. This course is highly appropriate for an aspiring psychology researcher, as it specifically covers the foundational statistical concepts used in psychological research. The course's focus on null hypothesis significance testing, z-scores, effect size, and confidence intervals directly applies to the methodological analysis employed by psychologists. The modules on sampling distributions and the central limit theorem help form the basis of understanding the statistical underpinnings of psychological research. This course may help build the foundation for statistical analysis in psychology research.

See salaries and explore the career path for Psychology Researcher

Social Science Researcher

Social science researchers investigate various aspects of human society using quantitative and qualitative methods. This course is highly relevant as it specifically targets those interested in data analysis in the social sciences. The course is useful for learning about null hypothesis significance testing. Social science research often relies on this crucial concept. The topics covered, such as probability, distributions, and statistical testing, are all widely applicable. A social science researcher may find the course's emphasis on z-scores and confidence intervals helpful. This course may help build the statistical foundation to conduct thorough social science research.

See salaries and explore the career path for Social Science Researcher

Research Assistant

A research assistant often supports senior researchers in conducting experiments and analyzing data. This course, which delves into the foundational concepts of probability and statistics, helps build a foundation for understanding the statistical methods used in research. The course also provides practical knowledge of z-scores, effect size, and confidence intervals, which are invaluable for data analysis. The 'Null Hypothesis Significance Testing' module is especially relevant, as many research projects rely on this process. A research assistant will benefit from the course's emphasis on the logic of null hypothesis significance testing as well as statistical analysis.

See salaries and explore the career path for Research Assistant

Survey Analyst

Survey analysts are responsible for designing, administering, and analyzing surveys. This course, with its focus on probability, distributions, and statistical testing, is helpful for those interested in roles as survey analysts. The course material on random sampling and its relation to the normal distribution may be useful for ensuring the validity of survey data. The practical statistical methods taught, including z-scores and confidence intervals, will help inform analysis. A survey analyst will use 'Null Hypothesis Significance Testing', as covered in this course, when evaluating the results. This course helps build a strong foundation for statistical work in survey analysis.

See salaries and explore the career path for Survey Analyst

Data Analyst

Data analysts interpret data to identify trends and solve problems. This course, with its focus on probability, distributions, and statistical testing, may be useful for those looking to pursue a career as a data analyst. Learning about the normal distribution, sampling distributions, and the central limit theorem may be useful for understanding the nuances of manipulating datasets. This course's modules on effect size and statistical significance are particularly applicable to the kind of work a data analyst performs. The ability to perform null hypothesis significance testing is a core skill that this course emphasizes.

See salaries and explore the career path for Data Analyst

Statistical Consultant

Statistical consultants provide expertise in statistical methods to clients across various fields. An understanding of probability and statistical testing, as covered in this course, helps those who hope for a future as a statistical consultant. This course may be useful for learning about the normal distribution, sampling distributions, and the central limit theorem. The emphasis on applying the z-test and understanding errors in null hypothesis testing directly addresses the daily tasks of a statistical consultant. The ability to evaluate statistical significance and interpret effect sizes are vital skills taught as well. This course may help build a foundation for a career as a statistical consultant.

See salaries and explore the career path for Statistical Consultant

Psychometrician

Psychometricians specialize in the design and analysis of psychological tests, and they need a strong foundation in statistical methods. This course, with its emphasis on probability and statistical testing, is an excellent starting point for anyone wanting to become a psychometrician. The course teaches students about null hypothesis significance testing. In addition, they learn about the logic of evaluating statistical significance, which is critical for psychometric work. The course's lessons on z-scores, effect size, and confidence intervals are helpful for analyzing test results. This course helps build the statistical knowledge a psychometrician requires.

See salaries and explore the career path for Psychometrician

Academic Researcher

Academic researchers conduct research in universities and often publish their findings in peer-reviewed papers. This course, with its emphasis on statistical methods including null hypothesis significance testing, is highly valuable for those who wish to pursue a career as an academic researcher. Academic researchers often use z-scores, effect size, and confidence intervals. This course may be useful for academic research as it introduces these necessary concepts. Moreover, the course covers material on the central limit theorem and the logic of hypothesis testing. This course helps build a foundation for conducting statistical research. An academic researcher often requires a graduate level degree.

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Market Research Analyst

Market research analysts use statistical analysis to understand consumer behavior and market trends. This course provides an understanding of statistical significance and data analysis, which are valuable tools in market research. The course's coverage of z-scores, effect size, and confidence intervals may be useful to market research studies. The emphasis on the logic of null hypothesis significance testing provides a vital framework for interpreting survey data. This course may help build the skills a market research analyst requires when evaluating the effectiveness of marketing campaigns and making informed recommendations. The module on 'Evaluating Statistical Significance' directly contributes to these tasks.

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Program Evaluator

Program evaluators assess the effectiveness of programs and policies, often using quantitative data. This course, with its emphasis on probability, distributions, and statistical testing, is helpful for those who wish to be program evaluators. The course's curriculum covers the logic of null hypothesis significance testing, which can help evaluators determine the impact of programs. The course's lessons on z-scores, effect size, and confidence intervals are valuable for analyzing program data. Program evaluators use statistical analysis, which this course helps to build a base for. This course may help build the skills a program evaluator needs.

See salaries and explore the career path for Program Evaluator

Quantitative Analyst

Quantitative analysts, often working in finance, use quantitative methods to understand markets and develop trading strategies. This course, while not specific to finance, provides a foundation in statistical methods, including null hypothesis testing, which can be adapted to quantitative analysis. The course's coverage of probability, the normal distribution, and sampling distributions are all vital for understanding numerical data. The 'Null Hypothesis Significance Testing' module helps build a foundational understanding for quantitative analysis. A quantitative analyst may find learning about effect size, confidence intervals, and statistical power helpful.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

Research scientists conduct research in various scientific fields, and many use quantitative methods. This course is relevant for research scientists who use statistical data. The course's modules on probability and statistical distributions help build a foundation for scientific research. The emphasis on the central limit theorem, null hypothesis significance testing, and statistical errors are valuable in research science. The course's lessons on the z-test, effect size, and confidence intervals will help anyone involved in quantitative data analysis. This course may help build a foundation for statistical work performed by research scientists. A research scientist often requires an advanced degree.

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Biostatistician

Biostatisticians apply statistical methods to biological and health-related data. This course, with a focus on probability, distributions, and statistical testing, may be useful for those pursuing a career as a biostatistician. Biostatisticians need a deep understanding of statistical analysis. This course introduces null hypothesis significance testing, which is vital to the kind of work a biostatistician conducts. The course's emphasis on the normal distribution, sampling distributions, and the central limit theorem is also useful. A biostatistician often relies on evaluating statistical significance and error. Thus, this course may help build a solid statistical foundation.

See salaries and explore the career path for Biostatistician

Data Scientist

Data scientists use advanced statistical techniques to extract insights from complex data. While this course introduces fundamental statistical concepts, it may be useful to the data scientist. The course will help to build a solid foundation in probability and the central limit theorem. The course material on the logic of null hypothesis testing is relevant. Learning about z-scores, effect size, and confidence intervals can be useful. For data scientists, understanding statistical significance and error is important. This course may help build the requisite base of knowledge in mathematics and statistics.

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Business Analyst

Business analysts use data to improve business processes and decision-making. This course, with its foundation in probability and statistical inference, may be useful for those hoping to become a business analyst. Understanding the normal distribution and the central limit theorem is helpful for interpreting business data. This course's teachings regarding statistical significance may be helpful. In addition, the ability to use z-scores, calculate effect size and confidence intervals may be helpful in improving business processes. Learning about null hypothesis testing may be useful for a business analyst.

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Intro to Null Hypothesis Significance Testing with z-test

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Foundational intro to null hypothesis testing

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