### TED Theater, Soho, New York

Tuesday, September 24, 2019
New York, NY

## The Event

As part of Global Goals Week, the Skoll Foundation and the United Nations Foundation are pleased to present We the Future: Accelerating Sustainable Development Solutions on September 21, 2017 at TED Theater in New York.
The Sustainable Development Goals, created in partnership with individuals around the world and adopted by world leaders at the United Nations, present a bold vision for the future: a world without poverty or hunger, in which all people have access to healthcare, education and economic opportunity, and where thriving ecosystems are protected. The 17 goals are integrated and interdependent, spanning economic, social, and environmental imperatives.
Incremental change will not manifest this new world by 2030. Such a shift requires deep, systemic change. As global leaders gather for the 72nd Session of the UN General Assembly in September, this is the moment to come together to share models that are transforming the way we approach the goals and equipping local and global leaders across sectors to accelerate achievement of the SDGs.

Together with innovators from around the globe, we will showcase and discuss bold models of systemic change that have been proven and applied on a local, regional, and global scale. A curated audience of social entrepreneurs, corporate pioneers, government innovators, artistic geniuses, and others will explore how we can learn from, strengthen, and scale the approaches that are working to create a world of sustainable peace and prosperity.

Meet the

# Speakers

Click on photo to read each speaker bio.

Amina

### Mohammed

Deputy Secretary-General of the United Nations

Astro

### Teller

Captain of Moonshots, X

Catherine

### Cheney

West Coast Correspondent, Devex

Chris

Debbie

### Aung Din

Co-founder of Proximity Designs

Dolores

### Dickson

Regional Executive Director, Camfed West Africa

Emmanuel

### Jal

Musician, Actor, Author, Campaigner

Ernesto

### Zedillo

Member of The Elders, Former President of Mexico

Georgie

### Benardete

Co-Founder and CEO, Align17

Gillian

### Caldwell

CEO, Global Witness

Governor Jerry

### Brown

State of California

Her Majesty Queen Rania

Jordan

Jake

### Wood

Co-founder and CEO, Team Rubicon

Jessica

### Mack

Senior Director for Advocacy and Communications, Global Health Corps

Josh

### Nesbit

CEO, Medic Mobile

Julie

### Hanna

Executive Chair of the Board, Kiva

Kate Lloyd

### Morgan

Producer, Shamba Chef; Co-Founder, Mediae

Kathy

### Calvin

President & CEO, UN Foundation

Mary

### Robinson

Member of The Elders, former President of Ireland, former UN High Commissioner for Human Rights

Maya

### Chorengel

Senior Partner, Impact, The Rise Fund

Dr. Mehmood

### Khan

Vice Chairman and Chief Scientific Officer, PepsiCo

Michael

### Green

CEO, Social Progress Imperative

### Yunus

Nobel Prize Laureate; Co-Founder, YSB Global Initiatives

Dr. Orode

### Doherty

Country Director, Africare Nigeria

### Muthiah

CEO, Global Alliance for Clean Cookstoves

Rocky

### Dawuni

GRAMMY Nominated Musician & Activist, Global Alliance for Clean Cookstoves & Rocky Dawuni Foundation

Safeena

### Husain

Founder & Executive Director, Educate Girls

Sally

### Osberg

President and CEO, Skoll Foundation

Shamil

### Idriss

President and CEO, Search for Common Ground

Main venue

### TED Theater

Soho, New York

330 Hudson Street, New York, NY 10013

#### Email

wtfuture@skoll.org

Due to limited space, this event is by invitation only.

## Save the Date

Write A One Sentence Explanation On The Condition And The Calculations. â¢ The paired differences d = x1- x2should be approximately normally distributed or be a large sample (need to check nâ¥30). Students should always think about that before they create any graph. 7.2 âSample Proportions Perform the test of Example $$\PageIndex{2}$$ using the $$p$$-value approach. That’s not verifiable; there’s no condition to test. By this we mean that the means of the y-values for each x lie along a straight line. But what does “nearly” Normal mean? Other assumptions can be checked out; we can establish plausibility by checking a confirming condition. A condition, then, is a testable criterion that supports or overrides an assumption. If the problem specifically tells them that a Normal model applies, fine. What Conditions Are Required For Valid Small-sample Inferences About Ha? Conditions required for a valid large-sample confidence interval for µ. More precisely, it states that as gets larger, the distribution of the difference between the sample average ¯ and its limit , when multiplied by the factor (that is (¯ â)), approximates the normal distribution with mean 0 and variance . All of mathematics is based on “If..., then...” statements. What kind of graphical display should we make – a bar graph or a histogram? Your statistics class wants to draw the sampling distribution model for the mean number of texts for samples of this size. White on this dress will need a brightener washing
Due to the Central Limit Theorem, this condition insures that the sampling distribution is approximately normal and that s will be a good estimator of Ï. We test a condition to see if it’s reasonable to believe that the assumption is true. Plausible, based on evidence. There’s no condition to test; we just have to think about the situation at hand. We already made an argument that IV estimators are consistent, provided some limiting conditions are met. If not, they should check the nearly Normal Condition (by showing a histogram, for example) before appealing to the 68-95-99.7 Rule or using the table or the calculator functions. False, but close enough. The data provide sufficient evidence, at the $$5\%$$ level of significance, to conclude that a majority of adults prefer the company’s beverage to that of their competitor’s. We already know the appropriate assumptions and conditions. Large Sample Assumption: The sample is large enough to use a chi-square model. General Idea:Regardless of the population distribution model, as the sample size increases, the sample meantends to be normally distributed around the population mean, and its standard deviation shrinks as n increases. Equal Variance Assumption: The variability in y is the same everywhere. The test statistic has the standard normal distribution. We confirm that our group is large enough by checking the... Expected Counts Condition: In every cell the expected count is at least five. where $$p$$ denotes the proportion of all adults who prefer the company’s beverage over that of its competitor’s beverage. Don’t let students calculate or interpret the mean or the standard deviation without checking the... Unverifiable. Perform the test of Example $$\PageIndex{1}$$ using the $$p$$-value approach. We first discuss asymptotic properties, and then return to the issue of finite-sample properties. For example: Categorical Data Condition: These data are categorical. and has the standard normal distribution. We can proceed if the Random Condition and the 10 Percent Condition are met. To test this belief randomly selected birth records of $$5,000$$ babies born during a period of economic recession were examined. Independent Trials Assumption: The trials are independent. By this we mean that there’s no connection between how far any two points lie from the population line. Determine whether there is sufficient evidence, at the $$10\%$$ level of significance, to support the researcher’s belief. A simple random sample is â¦ Independent Trials Assumption: Sometimes we’ll simply accept this. We know the assumption is not true, but some procedures can provide very reliable results even when an assumption is not fully met. Make checking them a requirement for every statistical procedure you do. When we have proportions from two groups, the same assumptions and conditions apply to each. Students will not make this mistake if they recognize that the 68-95-99.7 Rule, the z-tables, and the calculator’s Normal percentile functions work only under the... Normal Distribution Assumption: The population is Normally distributed. Question: Use The Central Limit Theorem Large Sample Size Condition To Determine If It Is Reasonable To Define This Sampling Distribution As Normal. Missed the LibreFest? As before, the Large Sample Condition may apply instead. Least squares regression and correlation are based on the... Linearity Assumption: There is an underlying linear relationship between the variables. We base plausibility on the Random Condition. The theorems proving that the sampling model for sample means follows a t-distribution are based on the... Normal Population Assumption: The data were drawn from a population that’s Normal. \begin{align} Z &=\dfrac{\hat{p} −p_0}{\sqrt{ \dfrac{p_0q_0}{n}}} \\[6pt] &= \dfrac{0.54−0.50}{\sqrt{\dfrac{(0.50)(0.50)}{500}}} \\[6pt] &=1.789 \end{align}. In case it is too small, it will not yield valid results, while a sample is too large may be a waste of both money and time. Each can be checked with a corresponding condition. With practice, checking assumptions and conditions will seem natural, reasonable, and necessary. To test this claim $$500$$ randomly selected people were given the two beverages in random order to taste. Require that students always state the Normal Distribution Assumption. Many students observed that this amount of rainfall was about one standard deviation below average and then called upon the 68-95-99.7 Rule or calculated a Normal probability to say that such a result was not really very strange. The test statistic follows the standard normal distribution. Of course, in the event they decide to create a histogram or boxplot, there’s a Quantitative Data Condition as well. The Normal Distribution Assumption is also false, but checking the Success/Failure Condition can confirm that the sample is large enough to make the sampling model close to Normal. Select All That Apply. If you survey 20,000 people for signs of anxiety, your sample size is 20,000. 2020 AP with WE Service Scholarship Winners, AP Computer Science A Teacher and Student Resources, AP English Language and Composition Teacher and Student Resources, AP Microeconomics Teacher and Student Resources, AP Studio Art: 2-D Design Teacher and Student Resources, AP Computer Science Female Diversity Award, Learning Opportunities for AP Coordinators, Accessing and Using AP Registration and Ordering, Access and Initial Setup in AP Registration and Ordering, Homeschooled, Independent Study, and Virtual School Students and Students from Other Schools, Schools That Administer AP Exams but Don’t Offer AP Courses, Transfer Students To or Out of Your School, Teacher Webinars and Other Online Sessions, Implementing AP Mentoring in Your School or District. If those assumptions are violated, the method may fail. We will use the critical value approach to perform the test. The population is at least 10 times as large as the sample. As was the case for two proportions, determining the standard error for the difference between two group means requires adding variances, and that’s legitimate only if we feel comfortable with the Independent Groups Assumption. If so, it’s okay to proceed with inference based on a t-model. As always, though, we cannot know whether the relationship really is linear. Instead students must think carefully about the design. Watch the recordings here on Youtube! Consider the following right-skewed histogram, which records the number of pets per household. 10% Condition B. Randomization Condition C. Large Enough Sample Condition A soft drink maker claims that a majority of adults prefer its leading beverage over that of its main competitor’s. We can never know if this is true, but we can look for any warning signals. when samples are large enough so that the asymptotic approximation is reliable. There is one formula for the test statistic in testing hypotheses about a population proportion. Examine a graph of the differences. The same test will be performed using the $$p$$-value approach in Example $$\PageIndex{1}$$. We can never know whether the rainfall in Los Angeles, or anything else for that matter, is truly Normal. The same is true in statistics. Which of the conditions may not be met? We don’t really care, though, provided that the sample is drawn randomly and is a very small part of the total population – commonly less than 10 percent. The same test will be performed using the $$p$$-value approach in Example $$\PageIndex{3}$$. Both the critical value approach and the p-value approach can be applied to test hypotheses about a population proportion p. The null hypothesis will have the form $$H_0 : p = p_0$$ for some specific number $$p_0$$ between $$0$$ and $$1$$. Close enough. They check the Random Condition (a random sample or random allocation to treatment groups) and the 10 Percent Condition (for samples) for both groups. We can develop this understanding of sound statistical reasoning and practices long before we must confront the rest of the issues surrounding inference. We have to think about the way the data were collected. Verify whether n is large enough to use the normal approximation by checking the two appropriate conditions.. For the above coin-flipping question, the conditions are met because n â p = 100 â 0.50 = 50, and n â (1 â p) = 100 â (1 â 0.50) = 50, both of which are at least 10.So go ahead with the normal approximation. Matching is a powerful design because it controls many sources of variability, but we cannot treat the data as though they came from two independent groups. Note that there’s just one histogram for students to show here. Searchable email properties. Since proportions are essentially probabilities of success, we’re trying to apply a Normal model to a binomial situation. Either the data were from groups that were independent or they were paired. Among them, $$270$$ preferred the soft drink maker’s brand, $$211$$ preferred the competitor’s brand, and $$19$$ could not make up their minds. What Conditions Are Required For Valid Large-sample Inferences About Ha? Normal Distribution Assumption: The population of all such differences can be described by a Normal model. Tossing a coin repeatedly and looking for heads is a simple example of Bernoulli trials: there are two possible outcomes (success and failure) on each toss, the probability of success is constant, and the trials are independent. Remember that the condition that the sample be large is not that n be at least 30 but that the interval [Ëp â 3âËp(1 â Ëp) n, Ëp + 3âËp(1 â Ëp) n] lie wholly within the interval [0, 1]. Globally the long-term proportion of newborns who are male is $$51.46\%$$. Check the... Nearly Normal Residuals Condition: A histogram of the residuals looks roughly unimodal and symmetric. Inference is a difficult topic for students. When we are dealing with more than just a few Bernoulli trials, we stop calculating binomial probabilities and turn instead to the Normal model as a good approximation. In other words, conclusions based on significance and sign alone, claiming that the null hypothesis is rejected, are meaningless unless interpreted â¦ It relates to the way research is conducted on large populations. The data do not provide sufficient evidence, at the $$10\%$$ level of significance, to conclude that the proportion of newborns who are male differs from the historic proportion in times of economic recession. We never see populations; we can only see sets of data, and samples never are and cannot be Normal. However, if the data come from a population that is close enough to Normal, our methods can still be useful. In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. $Z=\dfrac{\hat{p} −p_0}{\sqrt{ \dfrac{p_0q_0}{n}}}$. Question: What Conditions Are Required For Valid Large-sample Inferences About His? The point in the parameter space that maximizes the likelihood function is called the maximum likelihood estimate. We need to have random samples of size less than 10 percent of their respective populations, or have randomly assigned subjects to treatment groups. 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