This argument assumes that many people are actually using the carriage roads from March 15th to May 1st. What if virtually no one visited the park that time of year? Then this plan would do very little to prevent damage from overuse. Therefore the answer is "Whether a considerable percentage of carriage road usage occurs from March 15th to May 1st." – if a large percentage of use occurs during this time period then it’s a good plan, and if a small percentage of use occurs during this time period then it’s a bad plan. The relative damage caused by bikes and horses versus runners and runners and walkers is not important (both are being limited and you don’t know the real difference in the limitation). Whether snowmobiles are allowed in winter does not relate to the efficacy of this specific plan – maybe only a few snowmobiles use the roads. "Whether some sections of the carriage roads are more susceptible to damage from overuse than others." is pretty much given already in the stimulus and "Whether a substantial percentage of visitors to the park ride their bikes on the carriage roads during their visit." is also not relevant as you don’t need to know how popular biking is in relation to the total population. Answer is "Whether a considerable percentage of carriage road usage occurs from March 15th to May 1st.".

Given that the argument seems almost self-evidently true (62.5% is much more than 16.7%), the real question in this "useful to evaluate" problem is: "How could the conclusion about fans having unmet expectations possibly be false?"

There's a very subtle disconnect in the statistics given by this question; the numbers don't actually offer the right sort of information. Specifically, there's a different between discussing the number or percentage of teams that will win, on the one hand, and discussing the number or percentage of fans whose teams will win, on the other hand. If, for instance, nearly all fans share the same favorite baseball team, then it would be quite possible for nearly all of the fans to root for a winner even as only one of 30 teams actually wins.

A simple example with numbers: Imagine 8 fans, of whom five root for the Yankees, one roots for the Dodgers, one roots for the Braves, and one roots for the Tigers. The other 26 teams have no fans. If the Yankees win the World Series, only 1/30, or about 3%, of the baseball teams in the league won the championship. But fully 5/8, or 62.5%, of the fans got to see their favorite team win and their championship expectations met.

In the face of this disconnect, we need to know whether such an uneven distribution of fandom actually exists. If it does, then the fans' expectations could be met. If, instead, the distribution of fandom is relatively even, then indeed the argument's conclusion will hold as most of the fans will see their expectations go unmet. Thus "Whether each team has roughly the same number of fans." is correct.

"Whether baseball will drastically change the number of teams that qualify for the playoffs during the next five years." is not relevant, since regardless of now many teams may qualify for the playoffs we are told that only one team can win the championship each year.

"How many teams are good enough to be considered championship contenders in an average year." sounds nice, but the conclusion is about "championship[s]," and being a contender just isn't good enough.

"Whether a single team is likely win the World Series multiple times during the next five years." doubles down on the numerical flaw. If a single team wins multiple championships, then the number of distinct championship teams over the five year span would actually comprise less than 16.7% of all of the teams, potentially leaving even more fan expectations unmet.

"How many current fans will continue to follow their teams if their championship expectations are not met." simply does not address the conclusion. At no point do we care how fans might react to having their expectations frustrated.

When you are asked to determine which experiment would yield useful results for analyzing a particular problem, recognize that this means that you are dealing with a (slightly less obvious) Useful to Evaluate problem. With any Useful to Evaluate problem, remember that you first want to look at the stimulus itself to figure out what the scientists or analysts are trying to determine. Then look for the gap - what piece of information is missing that could potentially either prove their hypothesis or disprove it?

The stimulus states that mice treated with statins experienced drug-induced muscular degeneration and that the scientists think this might have been cause by the statins over-activating something called creatine kinase (which is known to cause muscular degeneration). Notice that the hypothesis has to do with cause and effect: statins cause an over-activation of creatine kinase, which causes muscular degeneration. But what if the statins directly caused the degeneration instead of causing that intermediate step? Choice "Injecting mice with a creatine kinase inhibitor before administering the statins and then monitoring muscle tissue response." is the only option that accounts for this possibility. By first administering a creatine kinase blocker, scientists can ensure that the effect is caused because the statins activate the creatine kinase rather than because the statins directly damage muscles. Choice "Injecting mice with a creatine kinase inhibitor before administering the statins and then monitoring muscle tissue response." is therefore correct.

Among the other answers, choices "Administering statins to mice with increased creatine kinase activity and observing creatine kinase activity." and "Injecting mice with muscle repair medication and then monitoring levels of muscular response." can be eliminated because they don't mention creatine kinase at all. Choice "Injecting mice with creatine kinase inhibitors and then monitoring muscle tissue response." can be eliminated because it doesn't mention the administering the statin. Since you're looking for the mechanism by which the statin causes muscular degeneration. Choice "Measuring mice’s level of muscular myopathy, administering a creatine kinase inhibitor, and then measuring myopathy once more." can be eliminated because it's already known that creatine kinase can cause muscular myopathy - you don't need to establish that again, so "Measuring mice’s level of muscular myopathy, administering a creatine kinase inhibitor, and then measuring myopathy once more." can be eliminated.

For any Useful to Evaluate question, you should look for the piece of information that, depending on the answer to the additional information, would exploit a gap between the premise and the suggested course of action. To do that, you first need to understand the stimulus itself.

The stimulus states that bacterial infection is a dangerous problem for manufacturers. Chemical sterilizers are also very expensive, so analysts have suggested using UV lamps to do the same job since a single UV lamp costs the same as a month's worth of chemical sterilizers that could be used to sterilize the same number of vats. You are then asked what would be useful in figuring out whether the suggestion would help determine whether the change would decrease the cost of food sterilization. (The key here is that it saves money - not that it's safer or more effective.)

The only place that the stimulus discusses cost is where it states that a lamp costs the same as a month of chemical sterilizers in order to sterilize the same number of vats. If the lamps last longer than a month, then they will fulfill the requirement since the lamps will therefore be cheaper than chemical sterilizers. However, if the lamps only last a month (or less) then the lamps will be either the same cost or more expensive than the chemical sterilizers. Therefore the correct answer must be "Whether the UV lamps tend to be effective for longer than a single month.".

Among the other answers, you can eliminate "Whether food distributors should also sterilize their trucks in order to keep food safe." and "Whether appropriate cooking practices can effectively destroy bacteria once food has been shipped to consumers." since food safety and other points on the production chain aren't under discussion. Similarly, "Whether UV radiation is harmful to workers who come in contact with it." can be eliminated because worker safety is not a consideration within the argument - only cost is considered. Choice "Whether stainless steel containers are more prone to contamination than are other types of containers." can also be eliminated since the analyst is considering replacing the chemical sterilizers and does not discuss the possibility of using a different type of container.

This argument features issues with two extremely common logical fallacies: 1) correlation vs. causation and 2) data pools that aren't necessarily comparable. If you see these elements in the gap in logic, you can anticipate the right answer.

First, notice that in the 20 years between arrest statistics that the official cites, many things could have occurred other than the laws she cites. What if, for example, alcohol tariffs made the price so exorbitant that everyone just quit drinking? Or the city built a system of canals and everyone just kayaks around town now? There could well be other causes for the statistic - the laws might be correlated with the time period, but did they really cause the outcome?

Second, notice that the use of actual-number data (25,000 arrests vs. 18,000 arrests) doesn't necessarily tie to the conclusion. Yes the number of arrests down (and the total population is up), but the conclusion is that the legislation was successful in "preventing motorists from driving under the influence." Since "motorists" is a subset of the total population, you'd really want to see a statistic that isn't just total number of arrests, but something more like arrests per 1,000 motorists" - a statistic that accounts for the fact that the number of motorists could be way down (in which case "motorists" - those who still drive - might still be driving under the influence quite frequently, but the overall statistic is down because there are simply much fewer drivers).

Given those errors in the argument, choice "Increased access to public transportation and ride-sharing applications has cut the number of drivers in the province by more than half." is correct - it shows that the number of motorists is down, and supplies an alternate cause for the drop in the number of arrests. People are using Lyft and taking the train, not driving anymore.

Among the other choices:

"The population in her province has increased at a lower rate than the populations of neighboring provinces." is irrelevant, as whether the population has grown at a high or low rate compared to other provinces, the fact remains that the population has still increased. (And really what you want to know is the number of drivers/motorists)

"The new legal measures have increased the province's law enforcement costs at nearly twice the rate that tax receipts have increased." seems like it should matter (is this a good use of money?) but remember that the specific conclusion is only about whether the laws worked, not about whether they were a wise use of funding. Always stay within the specific scope of the conclusion!

"Since 1998, the number of lawyers focusing on defending those arrested for driving under the influence has more than doubled." misses the mark because of its timing - the statistic used in the argument is about arrests, and notes that this intervention of lawyers occurs after the arrests have already taken place. If lawyers were acting before the arrests, that might suggest that the lawyers are causing the reduction in the number even though people are still drinking and driving, but that's not the case here - the lawyers in "Since 1998, the number of lawyers focusing on defending those arrested for driving under the influence has more than doubled." don't come into the picture early enough to explain away the number of arrests.

And "The number of restaurants and pubs permitted to sell alcohol in the province has increased since 1998 at approximately the same rate of the province's population." is similar - if the number of establishments serving alcohol were way down that might be part of an alternate explanation for the reduction in arrests, but with the number of restaurants and pubs serving alcohol increasing, that's not the case.

As with any weaken question, your first goal should be to understand the argument presented and find the gap between the information given and the conclusion presented. In this argument, you are told that a study claims that not eating genetically modified foods (GMOs) leads to lower rates of type 2 diabetes and heart disease because people who didn't eat GMOs tend to develop type 2 diabetes and heart disease at a lower rate than do people who do eat GMOs.

The gap here is in the difference between correlation and causation. While there is a correlation between the behavior (not eating GMOs) and the outcome (not getting type 2 diabetes or heart disease), there is nothing that proves the outcome is due to the behavior. What if non-GMO foods were only available to individuals who were wealthy or who belonged to a specific ethnic group that developed both diseases at lower rates? The correct answer will exploit the gap between correlation and causation.

The only answer choice to do this is "Individuals who do not eat genetically modified foods also tend to exercise and make other healthy lifestyle choices associated with a decreased risk of both diseases.". If individuals who don't eat GMOs also engage in other activities known to lower the risk of both diseases, then it's impossible to tell if their decreased risk is due to the fact that they don't eat GMOs or the fact that they engage in these other activities. Maybe the cause of both (not eating GMOs and decreased risk) is their lifestyle, and those two effects are just correlated.

Among the other answers, "Genetically modified foods also tend to be organically grown, a process that uses fewer pesticides and artificial fertilizers than does conventional agriculture." can be eliminated because there is no way to link the practice of using pesticides or artificial fertilizers to diabetes or heart disease. Choice "Type II diabetes and heart disease are often exacerbated or triggered by poor diets, especially those high in processed grain-based foods and fat-heavy meat products." can be eliminated since it does not address anything to do with the conclusion, which is specifically about genetically modified foods. Choice "It is possible that some individuals could be naturally resistant to developing both type 2 diabetes and heart disease regardless of diet." can also be eliminated for the same reason - there is no reason that these individuals wouldn't have shown up in both groups. Choice "Other studies have examined the effects of organic food on the risk of developing both diseases, but not on the effects of genetically modified food." can be eliminated as well since whether other studies have looked at this issue doesn't impact whether or not the conclusion is correct.

If you look for the logical flaw in the argument of this Strengthen question, you should see that it is one of generalization. Based on exactly one data point - a particular soccer star is less famous than 20 different basketball players - the argument draws the general conclusion that soccer players are not the most well-known athletes in the world. But Fabricio is only one player: if this were a Strengthen question you would want to show that he is an outlier on the less-famous side (meaning that most players are far more famous than he is, so this one data point is less relevant). But since this is a Weaken question, you want to show that Fabricio is one of the most famous soccer players, so that all other players are even less famous.

Choice "Fabricio is the most well-known soccer player in the world." does exactly that, and is therefore correct: if Fabricio is the most famous soccer player, and he's less famous than 20 basketball players, then the conclusion that soccer players are not the most famous athletes is a lot more likely - Fabricio isn't the one not-famous outlier while all the others are very famous. "Fabricio is the most well-known soccer player in the world." is correct.

Among the other answer choices: choice "AC Camarillo has won its league's championship the past three consecutive seasons." doesn't link Fabricio or AC Camarillo to being well-known. If AC Camarillo is a champion in a lesser-known league, then Fabricio isn't necessarily a particularly well-known player; if the team is the champion of an extremely popular league, then it's more likely that he's among the most well-known (but you still don't know for sure).

Choice "Fabricio is less well-known than the stars of several other professional soccer teams." weakens the argument by going the opposite direction of "Fabricio is the most well-known soccer player in the world." and saying that Fabricio isn't among the most famous players in the world. Choice "The most well-known basketball players are not always the best players on their teams." would slightly strengthen the argument if it were specific to the 20 players more famous than Fabricio, as then there would be even more basketball players who are more famous than Fabricio. But since it's a generic "the most famous players are not always the best on their teams" it doesn't add any more players more famous than Fabricio and therefore has no direct bearing on the argument. And choice "No professional basketball players are also professional soccer players.​" is similarly generic and therefore doesn't add any new data to further the argument (if it were that the most famous basketball players ARE also soccer players then it would have a direct bearing, but as written it does not).

This weaken problem features a common gap in logic: a fact is given (the cross-country GPA is higher than the average GPA) and then one plausible explanation is given as the only explanation for why that fact is true. This is essentially mixing correlation for causation: because two things occur together (runners have a lot of time to think, and runners have high GPAs, the time to think must cause the GPA). The best ways to weaken one of these arguments are to find an alternate explanation or to show that the causation is reversed (it's not that time spent running causes the high GPA, but rather the high GPA leads people to run).

And choice "The cross-country team is among the few teams that do not require a tryout, making it a popular activity for high-achieving students who want to list a sport on their college applications." does exactly that - it suggests that students with high GPAs are more likely to join the cross-country team. It's not that the running makes them better students, but instead good students tend to go toward running as a way to add an extracurricular activity to their application profile. That's why choice "The cross-country team is among the few teams that do not require a tryout, making it a popular activity for high-achieving students who want to list a sport on their college applications." is correct.

Among the wrong choices:

Choice "Cross-country running is more strategic than many realize, requiring runners to expend mental energy thinking about race tactics." should be tempting, but it does not directly attack the notion that runners have a lot of time to think. An activity can be strategic and still allow time to think. Choices "Many cross-country runners are also members of the track-and-field team, for which the average grade point average is actually lower than that of the school as a whole.", "Because the nearest cross-country trails are a long distance from the school, cross-country is one of the most time-consuming activities the school offers.", and "The cross country team's average grade point average has dropped each of the last three years." each try to attack the premise, making you think that the GPA shouldn't be as high as it is. "Many cross-country runners are also members of the track-and-field team, for which the average grade point average is actually lower than that of the school as a whole." does this by grouping cross-country runners with the other track-and-field athletes, for whom the GPA is lower. But we already know that the cross-country GPA is high, so it doesn't matter which other groups you could pool them with to lower their grades. Choice "Because the nearest cross-country trails are a long distance from the school, cross-country is one of the most time-consuming activities the school offers." similarly tries to provide a reason why you wouldn't think that the GPA would be high (it's such a time-consuming activity!) but again that doesn't matter - the GPA is high, so we're just trying to determine the cause. And choice "The cross country team's average grade point average has dropped each of the last three years." tries to do this by showing that the GPA is dropping, but again it's already been established that it's higher than average, and your only job is to determine why.

In this argument, there is a correlation between brain size and activity of the ant – those ants that gather nourishment (which tend to be older) have larger brains than those ants that nurture larvae (which tend to be younger ants). Because of this correlation, the author concludes that the cause for this difference in size must be the increased cognitive skills required for gathering. But where is the proof for this? Perhaps there is some other attribute relating to the ants that perform these different tasks and that is the reason for the difference. What if quite simply, older ants (which perform the gathering) have larger brains than younger ants (which perform the nurturing)? Then it simply has to do with age NOT the tasks they are performing. To weaken this argument, you are looking for some other plausible explanation for the different size brains and "The brains of older ants that are not involved in gathering nourishment are the same size as those counterparts of the same age that do gather nourishment." gives that perfectly, as it suggests that indeed the difference might be explained by age alone.

For "Ants that have spent more time gathering nourishment do not have considerably larger brains than do ants that have spent a much shorter time gathering." – this does not necessarily weaken the argument. The activity the ant performs could indeed be causing the difference, but at a certain threshold, more gathering does not further increase brain size. "Ants that have spent more time gathering nourishment do not have considerably larger brains than do ants that have spent a much shorter time gathering." can be true and it does not hurt the core position of the argument. Likewise for "The brains of older ants that stop gathering nourishment to take on other tasks do not become smaller.", the author is just stating that the INCREASE in brain size is caused by the different activities – if the brain doesn’t decrease in size after stopping the activities it does not affect this conclusion. For "In many species of ants, the brains of older ants are only minimally larger than the brains of younger ants.", the degree of the difference in size is not addressed in the argument so this choice has no impact on the quality of the author’s conclusion. Similarly for "Ants that have to travel farther to gather nourishment do not have considerably larger brains than do ants that do not have to travel far.", the distance traveled is not addressed in the argument, simply what activity the ants engage in. Correct answer is "The brains of older ants that are not involved in gathering nourishment are the same size as those counterparts of the same age that do gather nourishment.".

The key in this problem is to consider some flaw with the trend that the sociologist cites. In other words, what might indicate that the trend will not continue? Consider the following scenario: 10 years ago, most women who would normally have married at 22 start waiting to get married until they are forty. Over the next twenty years, the marriage rate would go down dramatically because women are waiting to get married (and the average marriage age is going up). However, when they do decide to get married, the rate will go back up again. If this were true it would show a huge flaw in the sociologist’s reasoning so "The average age of marriage has increased dramatically in the past 20 years." is correct. For "Today’s divorce rates are expected to rise dramatically over the next 40 years." and "More women are expected to get married for a second and third time in the next 40 years." divorce rates and second/third time marriages are unimportant because the argument is only about first time marriages. "Many women are deciding to simply live with their partners rather than get married." and "Marriage is much less likely to occur today for the first time than it was in the 1960’s." would not indicate a flaw as they both seem to support the sociologist (that is the trend that marriage is disappearing). Answer is "The average age of marriage has increased dramatically in the past 20 years."