New SAT Reading : Graphs and Tables
Study concepts, example questions & explanations for New SAT Reading
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Example Questions
Example Question #21 : Graphs And Tables
The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.
In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in theensuing two centuries due to technology-driven productivity growth.
Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus's ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.
The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.
The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.
This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.
While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.
World Population, 1990-2015
|
YEAR |
NUMBER OF PEOPLE (in billions) |
|
1990 |
5.3 |
|
1993 |
5.5 |
|
1996 |
5.8 |
|
1999 |
6.1 |
|
2002 |
6.3 |
|
2005 |
6.4 |
|
2008 |
6.6 |
|
2010 |
6.8 |
|
2015 |
7.3 |
The above table plots the world population, in billions of people, from 1990 through 2015
Percent of Population Living in Extreme Poverty
|
1990 |
1993 |
1996 |
1999 |
2002 |
2005 |
2008 |
2010 |
2015 |
|
|
Europe and Central Asia |
2 |
3 |
4 |
4 |
2 |
2 |
1 |
3 |
1 |
|
Middle East |
8 |
6 |
5 |
5 |
5 |
5 |
5 |
6 |
5 |
|
Latin American and Caribbean |
11 |
10 |
10 |
12 |
14 |
9 |
5 |
5 |
4 |
|
East Asia and Pacific |
55 |
52 |
37 |
37 |
30 |
18 |
16 |
13 |
8 |
|
South Asia |
53 |
53 |
49 |
45 |
45 |
38 |
36 |
32 |
22 |
|
Sub-Saharan African |
57 |
60 |
48 |
59 |
57 |
51 |
48 |
47 |
42 |
The table above shows the percentage of people in each region that lived in extreme poverty, as defined by the World Bank, for each of the years plotted. The seventh region, North America, is not shown, as its extreme poverty rate fell below the minimum rate for tracking in this study.
Which of the following conclusions is best supported by the two tables?
In 1999, there were more people living in extreme poverty in South Asia than in East Asia and the Pacific.
Extreme poverty is not a major concern in Europe & Central Asia.
As of 2015, less than 3.5 billion people in the world lived in extreme poverty.
Fewer people in Latin America & the Caribbean lived in poverty in 2008 than in 2002.
As of 2015, less than 3.5 billion people in the world lived in extreme poverty.
To answer this question, we need to compare the answer options to the two tables. When you are looking at the answer options, keep in mind that the correct answer must be supported by both tables, not just one. Let’s start with “in 1999, there were more people living in extreme poverty in South Asia than in East Asia and the Pacific”. Note that the table that compares regions only deals with percents (percentage of people in that region living in poverty) but not with actual numbers of people. Without an idea of how many people live in each region, we can’t compare total numbers. Next, “fewer people in Latin America & the Caribbean lived in poverty in 2008 than in 2002” has the same problem - without knowing how many people are in each region, we can’t draw conclusions about the total number of people (for example, if one region has one billion people and another has one hundred, you could take the same percentage of each region and get wildly different actual numbers). Answer choice “extreme poverty is not a major concern in Europe & Central Asia” is interesting in that it offers a value judgment - from the numbers you might think “only 1% of people in this region live in extreme poverty, and 1% isn’t a big number” but ask yourself: if it’s 1% of 500 million people, isn’t it possibly a major concern to those 5 million people? Value judgments like this are very hard to prove, and when you’re asked to draw a conclusion on the SAT you need to be able to prove your answer. This leaves us with, “as of 2015, less than 3.5 billion people in the world lived in extreme poverty”. Without even having to go back and reference the tables you should notice that this choice mentions a population size- which is discussed in the first table- and poverty level- which is covered in the second table. To double check, we can look at the first table and see that is 2015 there are 7.3 billion people in the world- which makes 3.5 billion people about half of the population. Looking at the second table in 2015 we can see that all poverty rates are substantially below 50%, so we can draw this conclusion.
Example Question #22 : Graphs And Tables
The passage is adapted from Ngonghala CN, et. al’s “Poverty, Disease, and the Ecology of Complex Systems” © 2014 Ngonghala et al.
In his landmark treatise, An Essay on the Principle of Population, Reverend Thomas Robert Malthus argued that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty inevitable. The system of feedbacks that Malthus posited creates a situation similar to what social scientists now term a “poverty trap”: i.e., a self-reinforcing mechanism that causes poverty to persist. Malthus’s erroneous assumptions, which did not account for rapid technological progress, rendered his core prediction wrong: the world has enjoyed unprecedented economic development in theensuing two centuries due to technology-driven productivity growth.
Nonetheless, for the billion people who still languish in chronic extreme poverty, Malthus’s ideas about the importance of biophysical and biosocial feedback (e.g., interactions between human behavior and resource availability) to the dynamics of economic systems still ring true. Indeed, while they were based on observations of human populations, Malthus's ideas had reverberations throughout the life sciences. His insights were based on important underlying processes that provided inspiration to both Darwin and Wallace as they independently derived the theory of evolution by natural selection. Likewise, these principles underlie standard models of population biology, including logistic population growth models, predator-prey models, and the epidemiology of host-pathogen dynamics.
The economics literature on poverty traps, where extreme poverty of some populations persists alongside economic prosperity among others, has a history in various schools of thought. The most Malthusian of models were advanced later by Leibenstein and Nelson, who argued that interactions between economic, capital, and population growth can create a subsistence-level equilibrium. Today, the most common models of poverty traps are rooted in neoclassical growth theory, which is the dominant foundational framework for modeling economic growth. Though sometimes controversial, poverty trap concepts have been integral to some of the most sweeping efforts to catalyze economic development, such as those manifest in the Millennium Development Goals.
The modern economics literature on poverty traps, however, is strikingly silent about the role of feedbacks from biophysical and biosocial processes. Two overwhelming characteristics of under-developed economies and the poorest, mostly rural, subpopulations in those countries are (i) the dominant role of resource-dependent primary production—from soils, fisheries, forests, and wildlife—as the root source of income and (ii) the high rates of morbidity and mortality due to parasitic and infectious diseases. For basic subsistence, the extremely poor rely on human capital that is directly generated from their ability to obtain resources, and thus critically influenced by climate and soil that determine the success of food production. These resources in turn influence the nutrition and health of individuals, but can also be influenced by a variety of other biophysical processes. For example, infectious and parasitic diseases effectively steal human resources for their own survival and transmission. Yet scientists rarely integrate even the most rudimentary frameworks for understanding these ecological processes into models of economic growth and poverty.
This gap in the literature represents a major missed opportunity to advance our understanding of coupled ecological-economic systems. Through feedbacks between lower-level localized behavior and the higher-level processes that they drive, ecological systems are known to demonstrate complex emergent properties that can be sensitive to initial conditions. A large range of ecological systems—as revealed in processes like desertification, soil degradation, coral reef bleaching, and epidemic disease—have been characterized by multiple stable states, with direct consequences for the livelihoods of the poor. These multiple stable states, which arise from nonlinear positive feedbacks, imply sensitivity to initial conditions.
While Malthus’s original arguments about the relationship between population growth and resource availability were overly simplistic (resulting in only one stable state of subsistence poverty), they led to more sophisticated characterizations of complex ecological processes. In this light, we suggest that breakthroughs in understanding poverty can still benefit from two of his enduring contributions to science: (i) models that are true to underlying mechanisms can lead to critical insights, particularly of complex emergent properties, that are not possible from pure phenomenological models; and (ii) there are significant implications for models that connect human economic behavior to biological constraints.
World Population, 1990-2015
|
YEAR |
NUMBER OF PEOPLE (in billions) |
|
1990 |
5.3 |
|
1993 |
5.5 |
|
1996 |
5.8 |
|
1999 |
6.1 |
|
2002 |
6.3 |
|
2005 |
6.4 |
|
2008 |
6.6 |
|
2010 |
6.8 |
|
2015 |
7.3 |
The above table plots the world population, in billions of people, from 1990 through 2015
Percent of Population Living in Extreme Poverty
|
1990 |
1993 |
1996 |
1999 |
2002 |
2005 |
2008 |
2010 |
2015 |
|
|
Europe and Central Asia |
2 |
3 |
4 |
4 |
2 |
2 |
1 |
3 |
1 |
|
Middle East |
8 |
6 |
5 |
5 |
5 |
5 |
5 |
6 |
5 |
|
Latin American and Caribbean |
11 |
10 |
10 |
12 |
14 |
9 |
5 |
5 |
4 |
|
East Asia and Pacific |
55 |
52 |
37 |
37 |
30 |
18 |
16 |
13 |
8 |
|
South Asia |
53 |
53 |
49 |
45 |
45 |
38 |
36 |
32 |
22 |
|
Sub-Saharan African |
57 |
60 |
48 |
59 |
57 |
51 |
48 |
47 |
42 |
The table above shows the percentage of people in each region that lived in extreme poverty, as defined by the World Bank, for each of the years plotted. The seventh region, North America, is not shown, as its extreme poverty rate fell below the minimum rate for tracking in this study.
Which of the following best describes how the data in the two tables supports Malthus’s prediction that population growth will necessarily exceed the growth rate of the means of subsistence, making poverty an inevitable consequence?
It contradicts Malthus’s prediction, because it shows that poverty is decreasing even while the population is increasing.
It supports Malthus’s prediction, because it shows that poverty is still a major problem in the world.
It supports Malthus’s prediction, because it demonstrates that poverty is a problem that can be solved in certain regions.
It contradicts Malthus’s prediction, because it demonstrates that poverty remains highest in the same regions of the world year after year.
It contradicts Malthus’s prediction, because it shows that poverty is decreasing even while the population is increasing.
If we look at the first table, we can see that from 1990 to 2015 the world population did grow, which does support part of Malthus’s prediction. However, if we look at the second table, we see that from 1990-2015 the percentage of people living poverty actually decreases across each world region (and dramatically so in those regions that were >50% in 1990). Thus, as population increased, poverty decreased. This contradicts Mathus’s prediction overall so we can eliminate answer choices that claim to support his prediction: “ it supports Malthus’s prediction, because it shows that poverty is still a major problem in the world “ and “it supports Malthus’s prediction, because it demonstrates that poverty is a problem that can be solved in certain regions”.
Looking at the remaining options, “it contradicts Malthus’s prediction, because it demonstrates that poverty remains highest in the same regions of the world year after year” is a true statement based on the second table; however, this has nothing to do with Mathus’s prediction so we can eliminate it. This leaves us with, “it contradicts Malthus’s prediction, because it shows that poverty is decreasing even while the population is increasing” as the correct answer. This statement is supported by both of the tables and does contradict Malthus’s overall prediction because that tables show that as population increased, poverty decreased.
Example Question #1 : Interpreting Tables
The following is excerpted from a research article displaying the results of an analysis of the world population by country. (2001)
Which of the following can be concluded from the table above?
The total population grew by an increasing percent change in each of the years displayed in the table.
The total male population growth was greater than the total female population growth during the period from 1947 to 2000.
The annual rate of growth was greater in 2000 than it was in 1995.
The total population of Japan exceeded 100,000 for each of the years displayed in the table.
The total population of Japan exceeded 100,000 for each of the years displayed in the table.
When looking to draw conclusions from the data presented in a table, we’re looking for the conclusion that must be true based on the given information. Here, the percent change did not increase in each of the years - or, according to the table, in each 5 year period. So, this is not something we can conclude. We also don’t know that the annual rate of growth was greater in 2000 than in 1995. The annualized rate of growth given was higher in 1995 (.31) as compared to 2000 (.21). If we look to do the math, or even just broadly compare the change, we can see that the growth amount was higher for the female population between 1947 and 2000 than it was for the male population. We do, however, know that “The total population of Japan exceeded 100,000 for each of the years displayed in the table.” This may not seem immediately obvious - but keep in mind, the data is displayed in thousands! So, even the smallest total population (that of 1947) was 78,010,000 - well over 100,000!
Example Question #771 : New Sat
The information in the table best supports which of the following excerpts from the passage?
“If the price never strays from its intrinsic value, the title may not be appropriate.” (paragraph two)
“This phenomenon was so well-known that it has come to be the way many refer to an economic bubble.” (paragraph one)
“Comparisons of price fluctuations and their causes may suggest otherwise.” (paragraph two)
“The extent of the change in price certainly merits its status as an economic bubble.” (paragraph two)
“The extent of the change in price certainly merits its status as an economic bubble.” (paragraph two)
In this example, we’re looking to understand which option the table provides direct support for. Since the table does not include any information about either the conclusive vs. inconclusive nature of the percent data or any other factors that might come into play, and instead simply expresses the percent data, we can eliminate any option that addresses the idea that the “tulip mania” may not have represented an economic bubble. Additionally, while it is true that, based on the passage, “This phenomenon was so well-known that it has come to be the way many refer to an economic bubble,” we don’t have any direct support for this statement in the table. That said, the fact that the percent change both month-over-month and overall was highest for “tulip mania” as compared to all other economic bubbles supports the idea that “The extent of the change in price certainly merits its status as an economic bubble.”
Example Question #2 : Interpreting Tables
The following is excerpted from a sanitation budget overview of five cities in a particular region. The table shows the amount of money that each of five cities spent on sanitation in 2015 and the amount that each spent overall that year, with numbers shown in thousands of dollars. (2016)
Based on the information in the table above, which of the following can be concluded?
Chadwick spent the smallest percentage of its annual budget on sanitation.
Bronxton spent a higher percentage of its annual budget on sanitation than did Andersonville.
Andersonville spent a higher percentage of its annual budget on sanitation than did Edgeville.
Edgewater spent the largest percentage of its annual budget on sanitation.
Edgewater spent the largest percentage of its annual budget on sanitation.
In this question, we’re being asked to apply estimation and rounding to compare the percentage of its annual budget each city spent on sanitation. If we use a bit of estimation, we can round the percents, in fraction form, as follows:
Andersonville:
Bronxtown: 
Chadwick: 
Dodgeville:
Edgewater: 
So, by the process of elimination, we can see that Edgewater did in fact spend the largest percentage of its annual budget on sanitation. All incorrect answers are also quickly able to be disproven using the estimated fractions above.
Example Question #2 : Interpreting Tables
The table below gives sales information for the 20 bathroom cleaners in the United States in 2010. For each product, the table describes the brand of the product, the product type, fragrance, unit sales, percent change in unit sales since 2009, total dollar sales, percent change in dollar sales since 2009, average price of each unit sold, and the dollar change in price since 2009. The table is ordered by unit sales, from least to greatest.
Which of the following can be concluded from the information presented in the table?
Every orange-scented product experienced growth in unit sales from 2009 to 2010.
Every lemon scented bathroom cleaner sold more units in 2009 than in 2010.
The product with the highest unit sales in 2010 also had the highest percent change in dollar sales.
The product with the highest unit sales in 2010 also had the highest dollar sales.
Every orange-scented product experienced growth in unit sales from 2009 to 2010.
This table provides us with an abundance of information - far more than we could feasibly be asked about in any one question. So, process of elimination is our friend here. If we look to our correct answer, “Every orange scented product experienced growth in unit sales from 2009 to 2010,” we can see that in each row for “orange,” the “% change” in unit sales is positive. Thus, we’ve found our correct answer! We cannot, however, conclude that “The product with the highest unit sales also had the highest percent change in dollar sales.” This is a tricky one. Superclean Spray Fresh comes in at a 23.2% increase over the last year. If we scan the rest of the column for percent change in dollar sales, we can see that this is the highest percent *increase* but not the highest percent *change,* as Deluxe Powder Lemon experienced a 23.8% decrease during this period. We also cannot conclude that “The product with the highest unit sales also had the highest dollar sales.” If we again look to Superclean Spray Fresh and scan to see if its dollar sales are also the highest, we quickly see that they are surpassed by the next cleaner up, Ultrashine Powder Lemon. Finally, it is untrue that every lemon scented bathroom cleaner sold more units in 2009 than in 2010. If we scan for lemon cleaners, we can quickly see that several have a negative “% change” in unit sales.
Example Question #2 : Interpreting Tables
The table below gives sales information for the 20 bathroom cleaners in the United States in 2010. For each product, the table describes the brand of the product, the product type, fragrance, unit sales, percent change in unit sales since 2009, total dollar sales, percent change in dollar sales since 2009, average price of each unit sold, and the dollar change in price since 2009. The table is ordered by total dollar sales, from least to greatest.
The information in the table above best supports which of the following?
The product with the lowest dollar sales in 2010 also had the lowest average price.
Spray bathroom cleaners generated more dollar sales than any other single type of bathroom cleaner in 2010.
No product experienced growth in dollar sales but a decline in unit sales from 2009 to 2010.
The product with the highest dollar sales in 2010 also had the highest average price.
Spray bathroom cleaners generated more dollar sales than any other single type of bathroom cleaner in 2010.
This table provides us with an abundance of information - far more than we could feasibly be asked about in any one question. So, process of elimination is our friend here. The table is already sorted such that we can easily identify the products with the highest and lowest dollar sales. Here, we cannot conclude that “The product with the lowest dollar sales in 2010 also had the lowest average price,” since Incredible Spray Lemon, our lowest dollar sales for 2010 costs $2.90, but if we scan the column for price, we can see that Deluxe Aerosol Orange comes in at a lower average price of $2.61, as does Dirt Blaster Spray Orange at $2.80. We also cannot conclude that “No product experienced growth in dollar sales but a decline in unit sales from 2009 to 2010.” If we scan the portion of the table where “% change” in dollar sales is positive, we will still find cases where “% change” in units is negative (for instance, Mrs. Grime Spray Unscented). We can also disprove that “The product with the highest dollar sales in 2010 also had the highest average price,” Since Ultra Shine Powder Lemon rings in at $3.99, and several other products have average prices above $4.
We can, however, conclude that “Spray bathroom cleaners generated more dollar sales than any other single type of bathroom cleaner in 2010,” but we’ll need to do a bit of estimation, math, and comparison to get there. Our only job is to try to identify whether any other type could have outsold “spray” cleaners. Of our varieties, Powder cleaners are irrelevant - there are only two such cleaners and the leading cleaner doesn't have enough separation from the field to make it a runaway category. The decision to be made is between Spray and Aerosol, so your mission is to determine which sold more. You can effectively do this by looking to the gap between the two, rather than the total. Spray starts with two higher-sellers than Aerosol, earning approximately $33M and $27M to Aerosol's two biggest totals, $22M and $21M. This gives Spray a $17M "gap" that Aerosol needs to make up, and with the next highest seller also a Spray Cleaner, the gap widens to nearly $18M. From there, you can simply compare one-to-one the remaining Spray and Aerosol cleaners on the Dollar Sales list to see whether the gap will come down to something competitive. It does not - notice that further down the list the dollar figures become so small that they'll make little if any dent in that gap between Spray totals and Aerosol totals. Rather than calculate, you can logically call this one a runaway just by judging the gaps at the top of the list and noting the limited opportunities for Aerosol to close it down.
Example Question #23 : Graphs And Tables
The following is an excerpt from a publication on the origins of the term “Tulip Mania” and its use in today’s economics terminology. (2019)
The Dutch Tulip Bulb Market Bubble, otherwise known as “Tulip Mania,” refers to a period in the 1600s in which the price of tulip bulbs soared to an absurdly high price, in some cases over 6 times the average annual salary at the time. This phenomenon was so well-known that it has come to be the way many refer to an economic bubble - a situation where the price of an item becomes so inflated that it is considered disconnected from its intrinsic value.
But did “tulip mania” itself truly represent an actual “tulip mania,” or economic bubble? Comparisons of price fluctuations and their causes may suggest otherwise. While the extent of the change in price certainly merits its status as an economic bubble, if the price never strays from its intrinsic value, the title may not be appropriate. We must, thus, dig into the driving forces behind these changes in price to better understand Tulip Mania and its connection to the term “economic bubble.”
The table below provides the name, month-over-month percent changes, and overall percent increases of the five most well known “tulip manias” - or economic bubbles. The Economic Bubbles are listed in chronological order, with the first being the least recent, and the last being the most recent.
Based on the information in the passage and corresponding table, which of the following must be true?
Each economic bubble had an impact on its corresponding economy proportional to its overall percent increase from its pre-bubble price.
The five most common economic bubbles have each had a decreasing level of overall percent change chronologically.
The five most common economic bubbles have each had a decreasing level of month-over-month percent change chronologically.
Percent change may not be the only factor contributing toward the economic bubble status of an economic event.
Percent change may not be the only factor contributing toward the economic bubble status of an economic event.
When we’re asked to draw a conclusion that “must be true” based on the passage and/or table, we’ll want to use the process of elimination as a primary tactic. Here, we cannot determine that either month-by-month or in total, the level/percent change continue to decrease as we move chronologically through the listed events in the table. While this is true for the most part, in each case, we have a situation where this decreasing trend does not hold true. Additionally, we have no way of connecting the percent change to the impact on the economy, particularly because we don’t know how much the product at hand impacts the economy, or how long the bubble was in place for.
We can, however, conclude that “Percent change may not be the only factor contributing toward the economic bubble status of an economic event,” since the passage points out that despite the impressive level of percent change, a usual indicator of an economic bubble, “tulip mania” may not have, itself, qualified as one.
Example Question #3 : Interpreting Tables
The table below displays the percent of individuals who reported they believed in “ghosts or other supernatural beings” in 2000 and again in 2010. The table was part of a study conducted to determine cultural and religious impactors on the belief in the supernatural.
Which of the following is best supported by the information in the table?
The same number of people believed in ghosts in the Americas in 2000 as believed in ghosts in Africa during that time.
Belief in ghosts was more prevalent in Asia than in any other region in 2010.
More people believed in ghosts in Africa in 2010 than believed in them in Europe during that time.
For each region, belief in ghosts grew between 2000 and 2010.
Belief in ghosts was more prevalent in Asia than in any other region in 2010.
In this example, we need to be careful to address the type of data we’ve been presented with in the table. Here, we were given the percent of individuals in each region who report believing in the supernatural. So, without population data, we cannot draw any conclusions about the *number* of individuals in each group with this belief. (Remember not to bring any outside information into your analysis!) Thus, we can eliminate “The same number of people believed in ghosts in the Americas in 2000 as believed in ghosts in Africa during that time” and “More people believed in ghosts in Africa in 2010 than believed in them in Europe during that time.”
We also cannot conclude that “For each region, belief in ghosts grew between 2000 and 2010,” as there is a clear decrease in the percent of people who believe in ghosts in the region for both the Americas and Asia. We can, however, conclude that “Belief in ghosts was more prevalent in Asia than in any other region in 2010,” as a higher percentage of the population in Asia reported believing in the supernatural than did any other region in 2010. Keep in mind, even if you weren’t sure the meaning of the word “prevalent,” we can use context and process of elimination to arrive at the correct answer, as we have here.
Example Question #3 : Interpreting Tables
Which of the following, if true, would best support the data in the table?
In America, belief in the supernatural and astrological belief, an increasing trend between 2000 and 2010, tend to be strongly positively correlated with one another.
In Africa, an improvement in educational access in rural regions included a learning module explaining the science behind a phenomenon previously believed by many to be the work of the supernatural.
Europe has long been known to have a population with a particular affinity for belief in ghosts as compared to the other major regions of the world.
A widespread cultural trend toward television programs that hunt for and expose supernatural forces became popular in America during the period between 2000 and 2010.
In Africa, an improvement in educational access in rural regions included a learning module explaining the science behind a phenomenon previously believed by many to be the work of the supernatural.
In this example, we’re looking for an answer option that aligns with, and thus helps to explain, the data trends represented in the table. If “A widespread cultural trend toward television programs that hunt for and expose supernatural forces became popular in America during the period between 2000 and 2010,” we would expect this trend to increase belief in the supernatural - but such belief decreased in the Americas during this period, allowing us to eliminate this option. The fact that “Europe has long been known to have a population with a particular affinity for belief in ghosts as compared to the other major regions of the world,” since Europe had the lowest percentage who reported believing in the supernatural in both 2000 and in 2010. If “In America, belief in the supernatural and astrological belief, an increasing trend between 2000 and 2010, tend to be strongly positively correlated with one another,” we would again expect belief in the supernatural to have increased among Americans during this time. The data, however, shows us that this was not the case, so this option can also be eliminated.
This leaves us with our correct answer: “In Africa, an improvement in educational access in rural regions included a learning module explaining the science behind a phenomenon previously believed by many to be the work of the supernatural.” If access to education allowed for more widespread knowledge of the science behind a phenomenon previously believed to be the work of the supernatural, this could provide us with a rationale for why the percent of the population in Africa who reported believing in the supernatural decreased during this time.
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