Reading Standards for Informational Text > Stories Across Artistic Mediums (CCSS.RI.7.4) Practice Test

(1) When many students hear the word model, they picture a tiny volcano made of clay. That kind of model is real, but in science the word often has a wider meaning. A model can be a simplified representation of a system, such as a drawing that shows how energy flows in an ecosystem or an equation that predicts weather. A good model does not copy every detail; instead, it focuses on the parts that matter most so ideas can be tested and improved.

(2) Scientists conduct investigations by asking questions, forming a hypothesis, and gathering a data set. They then analyze what they observe to decide whether the pattern they see is significant1. Here, significant does not necessarily mean "important to me." It means the result is unlikely to happen by chance. Researchers may say, "The difference is significant," to signal that statistics suggest the effect is real. Of course, scientists still use hedges like might and likely because more tests can strengthen, or occasionally weaken, a claim.

(3) To explain their work, scientists aim to be transparent. Transparent writing is not see-through like glass; it is clear and open about methods, limits, and errors. A report might include the exact algorithm, or step-by-step procedure, used to clean the data, and it may label any variable that could change the outcome. Being transparent helps other people repeat the study and check whether the results hold up.

(4) Word choice also shapes tone. Calling a graph stubborn suggests the data resist easy explanation, while calling a pattern resilient praises how it holds up under different tests. Figurative language can be helpful if it guides the reader without misleading them. For instance, describing a model as a microscope for messy life implies that it helps us look closely at complicated situations. Still, writers must choose carefully: strong boosters like undoubtedly can sound confident, but if overused, they might push readers away.

Figure 1 (caption): The Investigation Cycle—Question → Hypothesis → Test → Analyze → Model Update.

Glossary: algorithm—an exact, step-by-step procedure for solving a problem; variable—something that can change or be changed; data set—a collection of related data; hypothesis—an informed, testable idea; model—a simplified representation of a system used to test ideas.

1 In statistics, significant means the pattern is unlikely to be due to random chance.

Paragraph 1: When you open a video app or news feed, you see a stream of items chosen just for you. Behind the scenes, an algorithm—a step-by-step procedure a computer follows—scores your clicks and watch time. The system looks for patterns, then predicts what you will likely enjoy next. It is sleek and fast, but it is not magic; it learns from data, which can be incomplete or skewed.

Paragraph 2: Companies say these systems curate content for convenience. Here, curate does not mean a person wearing a museum badge. It means selecting and arranging materials with a specific audience in mind. In practice, the software may boost certain topics and downplay others, aiming to keep you engaged. Over time, you might slip into a filter bubble, a personalized zone that can feel comfortable yet narrow. The tone of this process is careful and persuasive—it nudges rather than shouts.

Paragraph 3: You can push back. Try following a wider range of sources, or use settings that reset recommendations. Some platforms let you mark items as "not interested." These actions send a modest but real signal. While such steps may not erase all bias, they may open small windows in the bubble.

Figure 1: A simple flow: "Your click" → "Model updates" → "New recommendations." A side label reads "preferences inferred." A dotted line leads to a box labeled "settings and feedback."

Glossary: algorithm — a set of rules a computer follows to solve a problem; curate — to select and arrange with care; filter bubble — a limited information environment shaped by personalization.