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• A Researcher’s Guide To Statistical Significance And Sample Size Calculations

What Does It Mean for Research to Be Statistically Significant?

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What does it mean to be statistically significant, an example of null hypothesis significance testing, measuring statistical significance: understanding the p value (significance level), what factors affect the power of hypothesis test, 1. sample size, 2. significance level, 3. standard deviations, 4. effect size, why is statistical significance important for researchers, does your study need to be statistically significant, practical significance vs. statistical significance, part 1: how is statistical significance defined in research.

The world today is drowning in data.

That may sound like hyperbole but consider this. In 2018, humans around the world produced more than 2.5 quintillion bytes of data—each day. According to some estimates , every minute people conduct almost 4.5 million Google searches, post 511,200 tweets, watch 4.5 million YouTube videos, swipe 1.4 million times on Tinder, and order 8,683 meals from GrubHub. These numbers—and the world’s total data—are expected to continue growing exponentially in the coming years.

For behavioral researchers and businesses, this data represents a valuable opportunity. However, using data to learn about human behavior or make decisions about consumer behavior often requires an understanding of statistics and statistical significance.

Statistical significance is a measurement of how likely it is that the difference between two groups, models, or statistics occurred by chance or occurred because two variables are actually related to each other. This means that a “statistically significant” finding is one in which it is likely the finding is real, reliable, and not due to chance.

To evaluate whether a finding is statistically significant, researchers engage in a process known as null hypothesis significance testing . Null hypothesis significance testing is less of a mathematical formula and more of a logical process for thinking about the strength and legitimacy of a finding.

Imagine a Vice President of Marketing asks her team to test a new layout for the company website. The new layout streamlines the user experience by making it easier for people to place orders and suggesting additional items to go along with each customer’s purchase. After testing the new website, the VP finds that visitors to the site spend an average of $12.63. Under the old layout, visitors spent an average of $12.32, meaning the new layout increases average spending by $0.31 per person. The question the VP must answer is whether the difference of $0.31 per person is significant or something that likely occurred by chance.

To answer this question with statistical analysis, the VP begins by adopting a skeptical stance toward her data known as the null hypothesis . The null hypothesis assumes that whatever researchers are studying does not actually exist in the population of interest. So, in this case the VP assumes that the change in website layout does not influence how much people spend on purchases.

With the null hypothesis in mind, the manager asks how likely it is that she would obtain the results observed in her study—the average difference of $0.31 per visitor—if the change in website layout actually causes no difference in people’s spending (i.e., if the null hypothesis is true). If the probability of obtaining the observed results is low, the manager will reject the null hypothesis and conclude that her finding is statistically significant.

Statistically significant findings indicate not only that the researchers’ results are unlikely the result of chance, but also that there is an effect or relationship between the variables being studied in the larger population. However, because researchers want to ensure they do not falsely conclude there is a meaningful difference between groups when in fact the difference is due to chance, they often set stringent criteria for their statistical tests. This criterion is known as the significance level .

Within the social sciences, researchers often adopt a significance level of 5%. This means researchers are only willing to conclude that the results of their study are statistically significant if the probability of obtaining those results if the null hypothesis were true—known as the p value —is less than 5%.

Five percent represents a stringent criterion, but there is nothing magical about it. In medical research, significance levels are often set at 1%. In cognitive neuroscience, researchers often adopt significance levels well below 1%. And, when astronomers seek to explain aspects of the universe or physicists study new particles like the Higgs Boson they set significance levels several orders of magnitude below .05.

In other research contexts like business or industry, researchers may set more lenient significance levels depending on the aim of their research. However, in all research, the more stringently a researcher sets their significance level, the more confident they can be that their results are not due to chance.

Determining whether a given set of results is statistically significant is only one half of the hypothesis testing equation. The other half is ensuring that the statistical tests a researcher conducts are powerful enough to detect an effect if one really exists. That is, when a researcher concludes their hypothesis was incorrect and there is no effect between the variables being studied, that conclusion is only meaningful if the study was powerful enough to detect an effect if one really existed.

The power of a hypothesis test is influenced by several factors.

Sample size—or, the number of participants the researcher collects data from—affects the power of a hypothesis test. Larger samples with more observations generally lead to higher-powered tests than smaller samples. In addition, large samples are more likely to produce replicable results because extreme scores that occur by chance are more likely to balance out in a large sample rather than in a small one.

Although setting a low significance level helps researchers ensure their results are not due to chance, it also lowers their power to detect an effect because it makes rejecting the null hypothesis harder. In this respect, the significance level a researcher selects is often in competition with power.

Standard deviations represent unexplained variability within data, also known as error. Generally speaking, the more unexplained variability within a dataset, the less power researchers have to detect an effect. Unexplained variability can be the result of measurement error, individual differences among participants, or situational noise.   

A final factor that influences power is the size of the effect a researcher is studying. As you might expect, big changes in behavior are easier to detect than small ones.

Sometimes researchers do not know the strength of an effect before conducting a study. Even though this makes it harder to conduct a well powered study, it is important to keep in mind that phenomena that produce a large effect will lead to studies with more power than phenomena that produce only a small effect.

Statistical significance is important because it allows researchers to hold a degree of confidence that their findings are real, reliable, and not due to chance. But statistical significance is not equally important to all researchers in all situations. The importance of obtaining statistically significant results depends on what a researcher studies and within what context.

Within academic research, statistical significance is often critical because academic researchers study theoretical relationships between different variables and behavior. Furthermore, the goal of academic research is often to publish research reports in scientific journals. The threshold for publishing in academic journals is often a series of statistically significant results.

Outside of academia, statistical significance is often less important. Researchers, managers, and decision makers in business may use statistical significance to understand how strongly the results of a study should inform the decisions they make. But, because statistical significance is simply a way of quantifying how much confidence to hold in a research finding, people in industry are often more interested in a finding’s practical significance than statistical significance.

To demonstrate the difference between practical and statistical significance, imagine you’re a candidate for political office. Maybe you have decided to run for local or state-wide office, or, if you’re feeling bold, imagine you’re running for President.

During your campaign, your team comes to you with data on messages intended to mobilize voters. These messages have been market tested and now you and your team must decide which ones to adopt.

If you go with Message A, 41% of registered voters say they are likely to turn out at the polls and cast a ballot. If you go with Message B, this number drops to 37%. As a candidate, should you care whether this difference is statistically significant at a p value below .05?

The answer is of course not. What you likely care about more than statistical significance is practical significance —the likelihood that the difference between groups is large enough to be meaningful in real life.  

You should ensure there is some rigor behind the difference in messages before you spend money on a marketing campaign, but when elections are sometimes decided by as little as one vote you should adopt the message that brings more people out to vote. Within business and industry, the practical significance of a research finding is often equally if not more important than the statistical significance. In addition, when findings have large practical significance, they are almost always statistically significant too.

Conducting statistically significant research is a challenge, but it’s a challenge worth tackling. Flawed data and faulty analyses only lead to poor decisions. Start taking steps to ensure your surveys and experiments produce valid results by using CloudResearch. If you have the team to conduct your own studies, CloudResearch can help you find large samples of online participants quickly and easily. Regardless of your demographic criteria or sample size, we can help you get the participants you need. If your team doesn’t have the resources to run a study, we can run it for you. Our team of expert social scientists, computer scientists, and software engineers can design any study, collect the data, and analyze the results for you. Let us show you how conducting statistically significant research can improve your decision-making today.

Continue Reading: A Researcher’s Guide to Statistical Significance and Sample Size Calculations

Part 2: How to Calculate Statistical Significance

Part 3: Determining Sample Size: How Many Survey Participants Do You Need?

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How to Write a Conclusion for Research Papers (with Examples)

The conclusion of a research paper is a crucial section that plays a significant role in the overall impact and effectiveness of your research paper. However, this is also the section that typically receives less attention compared to the introduction and the body of the paper. The conclusion serves to provide a concise summary of the key findings, their significance, their implications, and a sense of closure to the study. Discussing how can the findings be applied in real-world scenarios or inform policy, practice, or decision-making is especially valuable to practitioners and policymakers. The research paper conclusion also provides researchers with clear insights and valuable information for their own work, which they can then build on and contribute to the advancement of knowledge in the field.

The research paper conclusion should explain the significance of your findings within the broader context of your field. It restates how your results contribute to the existing body of knowledge and whether they confirm or challenge existing theories or hypotheses. Also, by identifying unanswered questions or areas requiring further investigation, your awareness of the broader research landscape can be demonstrated.

Remember to tailor the research paper conclusion to the specific needs and interests of your intended audience, which may include researchers, practitioners, policymakers, or a combination of these.

Table of Contents

What is a conclusion in a research paper, summarizing conclusion, editorial conclusion, externalizing conclusion, importance of a good research paper conclusion, how to write a conclusion for your research paper, research paper conclusion examples.

• How to write a research paper conclusion with Paperpal? 

Frequently Asked Questions

A conclusion in a research paper is the final section where you summarize and wrap up your research, presenting the key findings and insights derived from your study. The research paper conclusion is not the place to introduce new information or data that was not discussed in the main body of the paper. When working on how to conclude a research paper, remember to stick to summarizing and interpreting existing content. The research paper conclusion serves the following purposes: 1

• Warn readers of the possible consequences of not attending to the problem.
• Recommend specific course(s) of action.
• Restate key ideas to drive home the ultimate point of your research paper.
• Provide a “take-home” message that you want the readers to remember about your study.

Types of conclusions for research papers

In research papers, the conclusion provides closure to the reader. The type of research paper conclusion you choose depends on the nature of your study, your goals, and your target audience. I provide you with three common types of conclusions:

A summarizing conclusion is the most common type of conclusion in research papers. It involves summarizing the main points, reiterating the research question, and restating the significance of the findings. This common type of research paper conclusion is used across different disciplines.

An editorial conclusion is less common but can be used in research papers that are focused on proposing or advocating for a particular viewpoint or policy. It involves presenting a strong editorial or opinion based on the research findings and offering recommendations or calls to action.

An externalizing conclusion is a type of conclusion that extends the research beyond the scope of the paper by suggesting potential future research directions or discussing the broader implications of the findings. This type of conclusion is often used in more theoretical or exploratory research papers.

Align your conclusion’s tone with the rest of your research paper. Start Writing with Paperpal Now!  

The conclusion in a research paper serves several important purposes:

• Offers Implications and Recommendations : Your research paper conclusion is an excellent place to discuss the broader implications of your research and suggest potential areas for further study. It’s also an opportunity to offer practical recommendations based on your findings.
• Provides Closure : A good research paper conclusion provides a sense of closure to your paper. It should leave the reader with a feeling that they have reached the end of a well-structured and thought-provoking research project.
• Leaves a Lasting Impression : Writing a well-crafted research paper conclusion leaves a lasting impression on your readers. It’s your final opportunity to leave them with a new idea, a call to action, or a memorable quote.

Writing a strong conclusion for your research paper is essential to leave a lasting impression on your readers. Here’s a step-by-step process to help you create and know what to put in the conclusion of a research paper: 2

• Research Statement : Begin your research paper conclusion by restating your research statement. This reminds the reader of the main point you’ve been trying to prove throughout your paper. Keep it concise and clear.
• Key Points : Summarize the main arguments and key points you’ve made in your paper. Avoid introducing new information in the research paper conclusion. Instead, provide a concise overview of what you’ve discussed in the body of your paper.
• Address the Research Questions : If your research paper is based on specific research questions or hypotheses, briefly address whether you’ve answered them or achieved your research goals. Discuss the significance of your findings in this context.
• Significance : Highlight the importance of your research and its relevance in the broader context. Explain why your findings matter and how they contribute to the existing knowledge in your field.
• Implications : Explore the practical or theoretical implications of your research. How might your findings impact future research, policy, or real-world applications? Consider the “so what?” question.
• Future Research : Offer suggestions for future research in your area. What questions or aspects remain unanswered or warrant further investigation? This shows that your work opens the door for future exploration.
• Closing Thought : Conclude your research paper conclusion with a thought-provoking or memorable statement. This can leave a lasting impression on your readers and wrap up your paper effectively. Avoid introducing new information or arguments here.
• Proofread and Revise : Carefully proofread your conclusion for grammar, spelling, and clarity. Ensure that your ideas flow smoothly and that your conclusion is coherent and well-structured.

Write your research paper conclusion 2x faster with Paperpal. Try it now!

Remember that a well-crafted research paper conclusion is a reflection of the strength of your research and your ability to communicate its significance effectively. It should leave a lasting impression on your readers and tie together all the threads of your paper. Now you know how to start the conclusion of a research paper and what elements to include to make it impactful, let’s look at a research paper conclusion sample.

How to write a research paper conclusion with Paperpal?

A research paper conclusion is not just a summary of your study, but a synthesis of the key findings that ties the research together and places it in a broader context. A research paper conclusion should be concise, typically around one paragraph in length. However, some complex topics may require a longer conclusion to ensure the reader is left with a clear understanding of the study’s significance. Paperpal, an AI writing assistant trusted by over 800,000 academics globally, can help you write a well-structured conclusion for your research paper. 

• Sign Up or Log In: Create a new Paperpal account or login with your details.  
• Navigate to Features : Once logged in, head over to the features’ side navigation pane. Click on Templates and you’ll find a suite of generative AI features to help you write better, faster.  
• Generate an outline: Under Templates, select ‘Outlines’. Choose ‘Research article’ as your document type.  
• Select your section: Since you’re focusing on the conclusion, select this section when prompted.  
• Choose your field of study: Identifying your field of study allows Paperpal to provide more targeted suggestions, ensuring the relevance of your conclusion to your specific area of research. 
• Provide a brief description of your study: Enter details about your research topic and findings. This information helps Paperpal generate a tailored outline that aligns with your paper’s content. 
• Generate the conclusion outline: After entering all necessary details, click on ‘generate’. Paperpal will then create a structured outline for your conclusion, to help you start writing and build upon the outline.  
• Write your conclusion: Use the generated outline to build your conclusion. The outline serves as a guide, ensuring you cover all critical aspects of a strong conclusion, from summarizing key findings to highlighting the research’s implications. 
• Refine and enhance: Paperpal’s ‘Make Academic’ feature can be particularly useful in the final stages. Select any paragraph of your conclusion and use this feature to elevate the academic tone, ensuring your writing is aligned to the academic journal standards. 

By following these steps, Paperpal not only simplifies the process of writing a research paper conclusion but also ensures it is impactful, concise, and aligned with academic standards. Sign up with Paperpal today and write your research paper conclusion 2x faster .  

The research paper conclusion is a crucial part of your paper as it provides the final opportunity to leave a strong impression on your readers. In the research paper conclusion, summarize the main points of your research paper by restating your research statement, highlighting the most important findings, addressing the research questions or objectives, explaining the broader context of the study, discussing the significance of your findings, providing recommendations if applicable, and emphasizing the takeaway message. The main purpose of the conclusion is to remind the reader of the main point or argument of your paper and to provide a clear and concise summary of the key findings and their implications. All these elements should feature on your list of what to put in the conclusion of a research paper to create a strong final statement for your work.

A strong conclusion is a critical component of a research paper, as it provides an opportunity to wrap up your arguments, reiterate your main points, and leave a lasting impression on your readers. Here are the key elements of a strong research paper conclusion: 1. Conciseness : A research paper conclusion should be concise and to the point. It should not introduce new information or ideas that were not discussed in the body of the paper. 2. Summarization : The research paper conclusion should be comprehensive enough to give the reader a clear understanding of the research’s main contributions. 3 . Relevance : Ensure that the information included in the research paper conclusion is directly relevant to the research paper’s main topic and objectives; avoid unnecessary details. 4 . Connection to the Introduction : A well-structured research paper conclusion often revisits the key points made in the introduction and shows how the research has addressed the initial questions or objectives. 5. Emphasis : Highlight the significance and implications of your research. Why is your study important? What are the broader implications or applications of your findings? 6 . Call to Action : Include a call to action or a recommendation for future research or action based on your findings.

The length of a research paper conclusion can vary depending on several factors, including the overall length of the paper, the complexity of the research, and the specific journal requirements. While there is no strict rule for the length of a conclusion, but it’s generally advisable to keep it relatively short. A typical research paper conclusion might be around 5-10% of the paper’s total length. For example, if your paper is 10 pages long, the conclusion might be roughly half a page to one page in length.

In general, you do not need to include citations in the research paper conclusion. Citations are typically reserved for the body of the paper to support your arguments and provide evidence for your claims. However, there may be some exceptions to this rule: 1. If you are drawing a direct quote or paraphrasing a specific source in your research paper conclusion, you should include a citation to give proper credit to the original author. 2. If your conclusion refers to or discusses specific research, data, or sources that are crucial to the overall argument, citations can be included to reinforce your conclusion’s validity.

The conclusion of a research paper serves several important purposes: 1. Summarize the Key Points 2. Reinforce the Main Argument 3. Provide Closure 4. Offer Insights or Implications 5. Engage the Reader. 6. Reflect on Limitations

Remember that the primary purpose of the research paper conclusion is to leave a lasting impression on the reader, reinforcing the key points and providing closure to your research. It’s often the last part of the paper that the reader will see, so it should be strong and well-crafted.

• Makar, G., Foltz, C., Lendner, M., & Vaccaro, A. R. (2018). How to write effective discussion and conclusion sections. Clinical spine surgery, 31(8), 345-346.
• Bunton, D. (2005). The structure of PhD conclusion chapters.  Journal of English for academic purposes ,  4 (3), 207-224.

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

Experience the future of academic writing – Sign up to Paperpal and start writing for free!  

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FINAL REPORT: COVID Select Concludes 2-Year Investigation, Issues 500+ Page Final Report on Lessons Learned and the Path Forward

The single most thorough review of the pandemic conducted to date

WASHINGTON – Today, the Select Subcommittee on the Coronavirus Pandemic concluded its two-year investigation into the COVID-19 pandemic and released a final report titled “ After Action Review of the COVID-19 Pandemic: The Lessons Learned and a Path Forward .” The final report will serve as a road map for Congress, the Executive Branch, and the private sector to prepare for and respond to future pandemics. Since February 2023, the Select Subcommittee has sent more than 100 investigative letters, conducted more than 30 transcribed interviews and depositions, held 25 hearings and meetings, and reviewed more than one million pages of documents. Members and staff have exposed high-level corruption in America’s public health system, confirmed the most likely origin of the pandemic, held COVID-19 bad actors publicly accountable, fostered bipartisan consensus on consequential pandemic-era issues, and more. This 520-page final report details all findings of the Select Subcommittee’s investigation.

“ This work will help the United States, and the world, predict the next pandemic, prepare for the next pandemic, protect ourselves from the next pandemic, and hopefully prevent the next pandemic. Members of the 119th Congress should continue and build off this work, there is more information to find and honest actions to be taken ,” wrote Chairman Wenstrup in a letter to Congress . “ The COVID-19 pandemic highlighted a distrust in leadership. Trust is earned. Accountability, transparency, honesty, and integrity will regain this trust. A future pandemic requires a whole of America response managed by those without personal benefit or bias. We can always do better, and for the sake of future generations of Americans, we must. It can be done .”

On Wednesday, December 4, 2024, at 10:30am, the Select Subcommittee will hold a markup of the final report and officially submit the report to the Congressional record. Ahead of the markup, the Select Subcommittee will also release additional supporting materials and recommendations.

The full, 520-page final report can be found here . A summary of the information can be found below:

The Origins of the Coronavirus Pandemic, Including but Not Limited to the Federal Government’s Funding of Gain-of-Function Research

COVID-19 ORIGIN : COVID-19 most likely emerged from a laboratory in Wuhan, China. The FIVE strongest arguments in favor of the “lab leak” theory include:

• The virus possesses a biological characteristic that is not found in nature.
• Data shows that all COVID-19 cases stem from a single introduction into humans. This runs contrary to previous pandemics where there were multiple spillover events.
• Wuhan is home to China’s foremost SARS research lab, which has a history of conducting gain-of-function research at inadequate biosafety levels.
• Wuhan Institute of Virology (WIV) researchers were sick with a COVID-like virus in the fall of 2019, months before COVID-19 was discovered at the wet market.
• By nearly all measures of science, if there was evidence of a natural origin it would have already surfaced.

PROXIMAL ORIGIN PUBLICATION : “The Proximal Origin of SARS-CoV-2” publication — which was used repeatedly by public health officials and the media to discredit the lab leak theory — was prompted by Dr. Fauci to push the preferred narrative that COVID-19 originated in nature.

GAIN-OF-FUNCTION RESEARCH : A lab-related incident involving gain-of-function research is most likely the origin of COVID-19. Current government mechanisms for overseeing this dangerous gain-of-function research are incomplete, severely convoluted, and lack global applicability.

ECOHEALTH ALLIANCE INC. (ECOHEALTH) : EcoHealth — under the leadership of Dr. Peter Daszak — used U.S. taxpayer dollars to facilitate dangerous gain-of-function research in Wuhan, China. After the Select Subcommittee released evidence of EcoHealth violating the terms of its National Institutes of Health (NIH) grant, the U.S. Department of Health and Human Services (HHS) commenced official debarment proceedings and suspended all funding to EcoHealth.

• New evidence also shows that the Department of Justice (DOJ) has opened an investigation into EcoHealth’s pandemic-era activities.

NIH FAILURES : NIH’s procedures for funding and overseeing potentially dangerous research are deficient, unreliable, and pose a serious threat to both public health and national security. Further, NIH fostered an environment that promoted evading federal record keeping laws — as seen through the actions of Dr. David Morens and “FOIA Lady” Marge Moore.

The Efficacy, Effectiveness, and Transparency of the Use of Taxpayer Funds and Relief Programs to Address the Coronavirus Pandemic, Including Any Reports of Waste, Fraud, or Abuse

COVID-19 RELIEF FUNDING : Federal and state governments had significant lapses in coordination, were unprepared to oversee the allocation of COVID-19 relief funds, and failed to sufficiently identify waste, fraud, and abuse of taxpayer dollars during the pandemic.

PAYCHECK PROTECTION PROGRAM : The Paycheck Protection Program — which offered essential relief to Americans in the form of loans that could be forgiven if the funds were used to offset pandemic-era hardships — was rife with fraudulent claims resulting in at least $64 billion of taxpayers’ dollars lost to fraudsters and criminals.

FRADULENT UNEMPLOYMENT CLAIMS : Fraudsters cost the American taxpayer more than $191 billion dollars by taking advantage of the federal government’s unemployment system and exploiting individuals’ personally identifiable information.

SMALL BUSINESS ADMINISTRATION (SBA) FAILURES : $200 million of taxpayers’ dollars were lost as a result of the SBA’s inability to conduct proper oversight, implement internal controls, and ensure fraud protection measures were enacted.

TRANSNATIONAL FRAUD : At least half of the taxpayer dollars lost in COVID-19 relief programs were stolen by international fraudsters.

COVID-19 RELIEF FUNDING OVERSIGHT : Expanding relief programs that lacked proper oversight functions exposed severe vulnerabilities in the system and paved the way for fraudsters, international criminals, and foreign adversaries to take advantage of taxpayers.

The Implementation or Effectiveness of Any Federal Law or Regulation Applied, Enacted, or Under Consideration to Address the Coronavirus Pandemic and Prepare for Future Pandemics

WORLD HEALTH ORGANIZATION (WHO) : The WHO’s response to the COVID-19 pandemic was an abject failure because it caved to pressure from the Chinese Communist Party and placed China’s political interests ahead of its international duties. Further, the WHO’s newest effort to solve the problems exacerbated by the COVID-19 pandemic — via a “Pandemic Treaty” — may harm the United States.

SOCIAL DISTANCING : The “6 feet apart” social distancing recommendation — which shut down schools and small business across the country — was arbitrary and not based on science. During closed door testimony, Dr. Fauci testified that the guidance, “sort of just appeared.”

MASK MANDATES : There was no conclusive evidence that masks effectively protected Americans from COVID-19. Public health officials flipped-flopped on the efficacy of masks without providing Americans scientific data — causing a massive uptick in public distrust.

LOCKDOWNS : Prolonged lockdowns caused immeasurable harm to not only the American economy, but also to the mental and physical health of Americans, with a particularly negative effect on younger citizens. Rather than prioritizing the protection of the most vulnerable populations, federal and state government policies forced millions of Americans to forgo crucial elements of a healthy and financially sound life.

NEW YORK PANDEMIC FAILURES : Former New York Governor Andrew Cuomo’s March 25 Order — which forced nursing homes to accept COVID-19 positive patients — was medical malpractice. Evidence shows that Mr. Cuomo and his Administration worked to cover up the tragic aftermath of their policy decisions in an apparent effort to shield themselves from accountability.

• Evidence suggests Mr. Cuomo knowingly and willfully made false statements to the Select Subcommittee on numerous occasions about material aspects of New York’s COVID-19 nursing home disaster and the ensuing cover-up. The Select Subcommittee referred Mr. Cuomo to the DOJ for criminal prosecution.

TRAVEL RESTRICTIONS : President Trump’s rapidly implemented travel restrictions saved lives. During Dr. Fauci’s transcribed interview, he unequivocally agreed with every travel restriction issued by the Trump Administration. This testimony runs counter to the public narrative that the Trump Administration’s travel restrictions were xenophobic.

COVID-19 MISINFORMATION : Public health officials often spread misinformation through conflicting messaging, kneejerk reactions, and a lack of transparency. In the most egregious examples of pervasive misinformation campaigns, off-label drug use and the lab leak theory were unjustly demonized by the federal government.

• The Biden Administration even employed undemocratic and likely unconstitutional methods — including pressuring social media companies to censor certain COVID-19 content — to fight what it deemed misinformation.

The Development of Vaccines and Treatments, and the Development and Implementation of Vaccination Policies for Federal Employees and Members of the Armed Forces

OPERATION WARP SPEED : President-elect Trump’s Operation Warp Speed — which encouraged the rapid development and authorization of the COVID-19 vaccine — was highly successful and helped save millions of lives.

COVID-19 VACCINE: Contrary to what was promised, the COVID-19 vaccine did not stop the spread or transmission of the virus.

RUSHED COVID-19 VACCINE APPROVAL: The FDA rushed approval of the COVID-19 vaccine in order to meet the Biden Administration’s arbitrary mandate timeline. Two leading FDA scientists warned their colleagues about the dangers of rushing the vaccine approval process and the likelihood of adverse events. They were ignored, and days later, the Biden Administration mandated the vaccine.

VACCINE MANDATES: Vaccine mandates were not supported by science and caused more harm than good. The Biden Administration coerced healthy Americans into compliance with COVID-19 vaccine mandates that trampled individual freedoms, harmed military readiness, and disregarded medical freedom to force a novel vaccine on millions of Americans without sufficient evidence to support their policy decisions.

NATURAL IMMUNITY: Public health officials engaged in a coordinated effort to ignore natural immunity — which is acquired through previous COVID-19 infection — when developing vaccine guidance and mandates.

VACCINE INJURY REPORTING SYSTEM: Vaccine injury reporting systems created confusion, failed to properly inform the American public about vaccine injuries, and deteriorated public trust in vaccine safety during the COVID-19 pandemic.

VACCINE INJURY COMPENSATION: The government is failing to efficiently, fairly, and transparently adjudicate claims for the COVID-19 vaccine injured.

The Economic Impact of the Coronavirus Pandemic and Associated Government Response on Individuals, Communities, Small Businesses, Health Care Providers, States, and Local Government Entities

BUSINESS IMPACTS : Federal and state governments imposed mandatory lockdowns that were the primary cause of temporary and permanent business closures. More than 160,000 businesses closed due to the pandemic — with 60% of those closures classified as permanent. For the businesses that stayed or re-opened, the lack of supply chain diversity exacerbated pandemic-era challenges and deepened existing disparities.

HEALTHCARE IMPACTS : America’s healthcare system was severely damaged by the COVID-19 pandemic. Patients experienced a decreased quality-of-care, longer wait times, shorter medical appointments, and missed diagnoses.

WORKER IMPACTS : Unemployment rates surged to levels not seen since the Great Depression. Overly broad mitigation measures — including the now debunked “6 feet apart” guidance — disproportionately impacted sectors with low wage earners.

FEDERAL RESERVE : The Federal Reserve’s aggressive, early, and unprecedented response to the COVID-19 pandemic prevented a severe economic downturn. This continued approach also contributed to staggering inflation.

The Societal Impact of Decisions to Close Schools, How the Decisions Were Made and Whether There is Evidence of Widespread Learning Loss or Other Negative Effects as a Result of These Decisions

COVID-19 SCHOOL CLOSURES : The “science” never justified prolonged school closures. Children were unlikely to contribute to the spread of COVID-19 or suffer severe illness or mortality. Instead, as a result of school closures, children experienced historic learning loss, higher rates of psychological distress, and decreased physical well-being.

CENTERS FOR DISEASE CONTROL AND PREVENTION (CDC) INFLUENCE : The Biden Administration’s CDC broke precedent and provided a political teachers organization with access to its scientific school reopening guidance. Former CDC Director Rochelle Walensky asked the American Federation of Teachers (AFT) to provide specific language for the guidance and even went so far as to accept numerous edits made by AFT.

AFT INFLUENCE : Schools remained closed longer than necessary because of AFT’s political interference in the CDC’s school reopening guidance. AFT is a political union, not a scientific organization, that advocated for mitigation efforts that prolonged school closures — including an automatic closure “trigger.”

• Testimony revealed that AFT President Weingarten had a direct telephone line to contact former CDC Director Walensky.

LONGTERM IMPACTS : Standardized test scores show that children lost decades worth of academic progress as a result of COVID-19 school closures. Mental and physical health concerns also skyrocketed — with suicide attempts by 12-17 year-aged girls rising 51%.

Cooperation By the Executive Branch and Others with Congress, the Inspectors General, the Government Accountability Office, and Others in Connection with Oversight of the Preparedness for and Response to the Coronavirus Pandemic

HHS OBSTRUCTION : The Biden Administration’s HHS engaged in a multi-year campaign of delay, confusion, and non-responsiveness in an attempt to obstruct the Select Subcommittee’s investigation and hide evidence that could incriminate or embarrass senior public health officials. It appears that HHS even intentionally under-resourced its component that responds to legislative oversight requests.

ECOHEALTH OBSTRUCTION : EcoHealth President Dr. Peter Daszak obstructed the Select Subcommittee’s investigation by providing publicly available information, instructing his staff to reduce the scope and pace of productions, and doctoring documents before releasing them to the public. Further, Dr. Daszak provided false statements to Congress.

DR. DAVID MORENS : Dr. Fauci’s Senior Advisor, Dr. David Morens, deliberately obstructed the Select Subcommittee’s investigation, likely lied to Congress on multiple occasions, unlawfully deleted federal COVID-19 records, and shared nonpublic information about NIH grant processes with EcoHealth President Dr. Peter Daszak.

NEW YORK OBSTRUCTION : New York’s Executive Chamber — led presently by Governor Kathy Hochul — redacted documents, offered numerous illegitimate privilege claims, and withheld thousands of documents without an apparent legal basis to obstruct the Select Subcommittee’s investigation into former Governor Cuomo’s pandemic-era failures.

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Question: 1. A research report concludes that there are significant differences among treatments, with “F(2, 24) = 8.62, p < .01.” If the same number of participants was used in all of the treatment conditions, then how many individuals were in each treatment? A. 10 B. 30 C. 7 D. 9 2. A research study comparing three treatments with n = 5 in each treatment produces

1. A research report concludes that there are significant differences among treatments, with “F(2, 24) = 8.62, p < .01.” If the same number of participants was used in all of the treatment conditions, then how many individuals were in each treatment? A. 10 B. 30 C. 7 D. 9

2. A research study comparing three treatments with n = 5 in each treatment produces T 1 = 5, T 2 = 10, T 3 = 15, with SS 1 = 6, SS 2 = 9, SS 3 = 9, and ΣX 2 = 94. For this study, what is SS between ? A.34 B.10 C.24 D.68

3. A researcher uses an independent-measures t test to evaluate the mean difference between two treatments and obtains t(12) = 4.00. If the researcher had used an ANOVA to evaluate the data, what F-ratio would be obtained?

1. To determine how many individuals were in each treatment, we need to look at the degrees of freed...

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