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For example: According to the passage, Woolf chooses the setting of the bridge because it A is conducive to a mood of fanciful reflection. B provides a good view of the procession of the sons of educated men. C is within sight of historic episodes to which she alludes. D is symbolic of the legacy of past and present sons of educated men. The relevant lines: Close at hand is a bridge over the River Thames, an admirable vantage ground for us to make a survey. The river flows beneath; barges pass, laden with timber, bursting with corn; there on one side are the domes and spires of the city; on the other, Westminster and the Houses of Parliament. It is a place to stand on by the hour, dreaming. But not now. Now we are pressed for time. Now we are here to consider facts; now we must fix our eyes upon the procession — the procession of the sons of educated men. To get the correct answer, B , you must deduce from the info given. My thoughts: Normally we'd be on the bridge to dream and have fanciful reflection, but that's not the case now.
Instead, now we have to do something else while standing on the bridge. What is that something else? Fixing our eyes on the procession of the sons of educated men. There will be a more full walkthrough of an inference question later on in this article — the point of that was to show the eensy, teensy step you have to take beyond what is written to answer inference questions.
In fact, I know that some people just think of these kinds of inference questions as paraphrasing, becaus so little interpretation is involved. Boulanger favors a form of education that emphasizes In Passage 1, Bach contends that breaking the laws of counterpoint has which consequence? Type 2: Speculation These inference questions ask you to speculate about the meaning or "suggested" meaning of a statement, description, or something else in the passage. In some ways, these are similar to function questions, but the answers the SAT is looking for are very different. Type 3: Examination Inference questions which fall into the examination subcategory question you about the internal life thoughts, feelings, motivations of the narrator, author, or someone mentioned in the passage. They can mostly be summed up as asking "What would [this person] think about [that thing]?
You will often see these sorts of questions on paired passages. Fortunately, it is pretty easy to identify examination inference questions, since they do tend to fall into "what does X think about Y" format. Designed and written by PrepScholar SAT experts, our SAT program customizes to your skill level in over 40 subskills so that you can focus your studying on what will get you the biggest score gains. Click on the button below to try it out! Is there any way other than just reading the entire passage? Answer to the first question: hold on one moment. Answer to that second question: yes, for sure I've compiled for you a bunch of different strategies for attacking inference questions. Some of these strategies work better if you read the passage a certain way for instance, looking for context may not be as necessary for students who read the passage all the way through , but much of the advice is useful no matter what your method of reading the passage.
Strategy 1: Understand What The Question Is Really Asking Understanding what an inference question is actually asking can be hard to wrap your head around, particularly when they ask about multiple passages. Questions are often wordy and seem to require a lot of information before you can even begin to answer them. For instance: The author of Passage 2 would most likely respond to the discussion of the future of space mining in lines , Passage 1, by claiming that such a future A is inconsistent with the sustainable use of space resources. B will be difficult to bring about in the absence of regulations. C cannot be attained without technologies that do not yet exist. This question requires you to find information about the future of space mining in Passage 1 and then put yourself in the shoes of the author of Passage 2 and figure out what that author would think about the topic …ugh. How do you answer this kind of question without causing your brain to hurt too much?
You break it down, piece by piece, into a question that is answerable. For the example above, you have to start by figuring out what the future of spacing mining says in Passage 1. In this case, the author of Passage 1 claims that not only will space miners might "[meet] earthly demands for precious metals As you can see, figuring out what an inference question is asking can get you most of the way to figuring out what the answer is. Let's look at the example from above again, keeping the three topics discussed in lines in mind. The author of Passage 2 would most likely respond to the discussion of the future of space mining in lines , Passage 1, by claiming that such a future A is inconsistent with the sustainable use of space resources. To find the answer to this question, use the process of elimination on the answer choices.
Do any of the parts of Passage 2 that address the claims in lines of Passage 1 support the choices? Choices C and D can be eliminated immediately because Passage 2 does not make either of these claims at all, which leaves you with a choice between choices A and B. Without consensus, claims will be disputed, investments risky, and the gains made insecure. Strategy 2: Look For Context SAT Reading may lull you into a sense of false security, because line numbers are given in a question so you clearly don't have to look elsewhere.
Sometimes even that isn't enough, and you'll need to know the main point of the passage or the perspective of the author in order to properly answer an inference question. If I don't come up with the answer in my own words before I look at the answer choices, I get tripped up by answer choices that include irrelevant information because I search for ways to prove that they are true - this is a waste of time and an easy way to get confused. Take this question: The author's attitude toward pharming is best described as one of A apprehension.
Maxine was insomniac. Peter was trying as hard as he could to stay awake but unfortunately fell asleep. Everyone has told him that he was real, so he had to make it sure with his own eyes. That's why he tried to stay awake at night but fell asleep at PM. Next thing he saw that it was already AM and presents all around in his room. Why was Peter trying so hard to stay awake at night? Maya had always counted on her grandfather for any kind of help and guidance. But now without him, she was feeling sad and lonely. With that feeling, it made her even more adamantly opposed to drunk driving. What was the obvious reason Maya was feeling that way? Related Topics.
Maxine was insomniac. Peter was trying as hard as he could to stay awake but unfortunately fell asleep. Everyone has told him that he was real, so he had to make it sure with his own eyes. That's why he tried to stay awake at night but fell asleep at PM. Next thing he saw that it was already AM and presents all around in his room. Why was Peter trying so hard to stay awake at night?
Maya had always counted on her grandfather for any kind of help and guidance. But now without him, she was feeling sad and lonely. With that feeling, it made her even more adamantly opposed to drunk driving. What was the obvious reason Maya was feeling that way? Related Topics.
Officially, we call this sampling from a distribution. Sampling is what you do at a grocery store when there is free food. You can keep taking more. However, if you take all of the samples, then what you have is called the population. Because we used the uniform distribution to create numbers, we already know where our numbers came from. However, we can still pretend for the moment that someone showed up at your door, showed you these numbers, and then you wondered where they came from.
Can you tell just by looking at these numbers that they came from a uniform distribution? What would need to look at? Perhaps you would want to know if all of the numbers occur with roughly equal frequency, after all they should have right? That is, if each number had the same chance of occurring, we should see that each number occurs roughly the same number of times. We already know what a histogram is, so we can put our numbers into a histogram and see what the counts look like. If all of the numbers occur with equal frequency, then each number should occur 10 times, because we sampled a total of numbers.
The histogram looks like this: Figure 5. All of the bars are not the same height. This shows that randomly sampling numbers from this distribution does not guarantee that our numbers will be exactly like the distribution they came from. We can call this sampling error, or sampling variability. We will sample 20 numbers from the uniform. Here we should expect that each number between 1 and 10 occurs two times each. So we will be looking at 10 histograms, each showing us what the 10 different samples of twenty numbers looks like: Figure 5. They all look quite different. The differences between the samples are due to sampling error You might notice right away that none of the histograms are the same.
Even though we are randomly taking 20 numbers from the very same uniform distribution, each sample of 20 numbers comes out different. This is sampling variability, or sampling error. Here is movie version. You are watching a new histogram for each sample of 20 observations. The horizontal line shows the shape of the uniform distribution. It crosses the y-axis at 2, because we expect that each number from 1 to 10 should occur about 2 times each in a sample of However, as you can see, this does not happen. Instead, each sample bounces around quite a bit, due to random chance. Figure 5. The black lines shows the expected number of times each number from 1 to 10 should occur. The fact that each number does not occur 2 times each illustrates the error associated with sampling Looking at the above histograms shows us that figuring out where our numbers came from can be difficult. In the real world, our measurements are samples. We usually only have the luxury of getting one sample of measurements, rather than repeating our own measurements 10 times or more.
If you look at the histograms, you will see that some of them look like they could have come from the uniform distribution: most of the bars are near two, and they all fall kind of on a flat line. But, if you happen to look at a different sample, you might see something that is very bumpy, with some numbers happening way more than others. But let me remind you, all of these samples came from a uniform distribution, this is what samples from that distribution look like. This is what chance does to samples, it makes the individual data points noisy.
Which of these two samples do you think came from a uniform distribution? The answer is that they both did. But, neither of them look like they did. Can we improve things, and make it easier to see if a sample came from a uniform distribution? Yes, we can. All we need to do is increase the sample-size. We will often use the letter n to refer to sample-size.
N is the number of observations in the sample. We will again create 10 samples each with observations , and make histograms for each of them. All of these samples will be drawn from the very same uniform distribution. This, means we should expect each number from 1 to 10 to occur about 10 times in each sample. Here are the histograms: Figure 5. So, we are still dealing with sampling error. Now we should expect every number to appear about times each. What happens? Each of these histograms are starting to flatten out. The bars are still not perfectly at , because there is still sampling error there always will be. But, if you found a histogram that looked flat and knew that the sample contained many observations, you might be more confident that those numbers came from a uniform distribution. Say , observations per sample. Here, we should expect that each number occurs about 10, times each. Now we see that all of our samples start to look the same.
They all have , observations, and this gives chance enough opportunity to equally distribute the numbers, roughly making sure that they all occur very close to the same amount of times. As you can see, the bars are all very close to 10,, where they should be if the sample came from a uniform distribution. Pro tip: The pattern behind a sample will tend to stabilize as sample-size increases. Small samples will have all sorts of patterns because of sampling error chance. First, which of these two samples do you think came from a uniform distribution? I will tell you that each of these samples had 20 observations each.
If you are not confident in the answer, this is because sampling error randomness is fuzzing with the histograms. Here is the very same question, only this time we will take 1, observations for each sample. Which one do you think came from a uniform distribution, which one did not? Now that we have increased N, we can see the pattern in each sample becomes more obvious. The histogram for sample 1 has bars near , not perfectly flat, but it resembles a uniform distribution. The histogram for sample 2 does not look flat at all.
Instead, there the number five appears most of the time, and numbers on either side of five happen less and less. Congratulations to Us! We have just made some statistical inferences without using formulas! Yes, by looking at our two samples we have inferred that sample 2 did not come from a uniform distribution. We have also inferred that sample 1 could have come form a uniform distribution. This is really all we will be doing for the rest of the course. We will be looking at some numbers, wondering where they came from, then we will arrange the numbers in such a way so that we can make an inference about where they came from. In an experiment we want to know if our independent variable our manipulation causes a change in our dependent variable measurement. If this occurs, then we will expect to see some differences in our measurement as a function of manipulation. Consider the light switch example: Light Switch Experiment: You manipulate the switch up condition 1 of independent variable , light goes on measurement.
You manipulate the switch down condition 2 of independent variable , light goes off another measurement. The measurement light changes goes off and on as a function of the manipulation moving switch up or down. You can see the change in measurement between the conditions, it is as obvious as night and day. So, when you conduct a manipulation, and can see the difference change in your measure, you can be pretty confident that your manipulation is causing the change. I hope so. We have already shown that the process of sampling numbers from a distribution is a chancy process that produces different samples. Different samples are different, so yes, chance can produce differences.
This can muck up our interpretation of experiments. It is very big, has thousands of gumballs. You want to find out if picking gumballs with your right hand vs. Plus, you will be blindfolded the entire time. The independent variable is Hand: right hand vs. The dependent variable is the measurement of the color of each gumball. You run the experiment as follows. Hopefully you will agree that your hands will not be able to tell the difference between the gumballs. So, what should happen in this experiment? Sort of yes, this is what you would usually get. But, it is not all that you can get.
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