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Identifying Trends, Patterns & Relationships in Scientific Data

Identifying Trends, Patterns & Relationships in Scientific Data
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  • 0:04 What Is Scientific Data?
  • 1:16 Looking at Trends
  • 2:10 Looking at Patterns
  • 3:09 Looking at Relationships
  • 4:20 Support or Refute?
  • 5:29 Lesson Summary
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Lesson Transcript
Instructor: Amanda Robb
In this lesson, we'll explore the difference between trends, patterns and relationships in scientific data. By the end of the lesson, you'll be able to identify these properties of data and explain how they support or refute a hypothesis.

What Is Scientific Data?

As you might already know, questions are the backbone of scientific investigations. What do plants need to grow? What proteins control cell division? How are traits inherited? Most questions about the natural world can be answered by collecting scientific data in experiments. Scientific data isn't just observations about a phenomenon, it's information gathered from experiments that are carefully designed to test one variable at a time.

Prior to starting an experiment, it's important to have a hypothesis based on background research. A hypothesis is an educated guess, or prediction, about what your experiment will show. Hypotheses usually take the form of ''if-then-because.'' If I change something then something will happen because.

After you collect your data, it's important to analyze your results and then go back to compare them to the hypothesis; but how exactly do you analyze data? What should you look for to support or refute your hypothesis? Scientists focus on three ways to analyze data: looking at trends, looking at patterns, and looking at relationships. To understand these forms of analysis, let's look at an example of each.

Looking at Trends

One of the most important ecological topics today is climate change. Scientists have been studying how surface temperatures have changed over more than 100 years. Every year there are some days where there are abnormally high temperatures and abnormally low temperatures. The data can be plotted as a scatterplot, where a dot is included for each temperature recorded. Although some days are high and some days are lower than average, overall the temperature is increasing each year.

Although there are some discrepancies in the data, overall global temperatures have increased over time
climate change data

This is a trend, the general tendency of a set of data to change. The data points may vary slightly, but overall the data moves in one direction. In the case of global warming, there is a trend of temperature increases over a long period of time. Climate change skeptics will argue that there are still cold days, so global warming can't be happening. But, when we examine the scientific data, we can see that overall the trend is that global temperature is increasing.

Looking at Patterns

Patterns in science are a little different. Data doesn't have to follow a trend, always going up or down over time. A pattern is a when data repeats in a predictable way. A good example of a pattern in science comes from the father of genetics, Gregor Mendel. Mendel was a scientist in the 1800s who studied the genetics of pea plants. He would breed pea plants with different characteristics and observe how these characteristics showed up in the next generation. When he bred a purebred yellow plant with a purebred green plant, he noticed that all plants were yellow.

This data showed up with other traits as well, such as plant size, pea pod shape, and flower color. One trait would mask the other. Mendel noticed this pattern in his experiments. This led Mendel to propose the idea of dominance - that some traits mask others. This is common mode of inheritance and explains many traits in humans such as eye color, ear shape, and hair lines.

Looking at Relationships

Relationships are similar to trends, except that the data has a clear mathematical relationship. Let's look at an example. In your physics lab you're examining the relationship between mass and force. Each time you apply a different mass to a spring scale and measure the force. When you apply 1kg of mass, you record a force of 10N. When you apply 2kg of mass, you record a force of 20N. Your data continues in this way for the remaining masses you weigh.

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