Science Courses/
- AuthorCoralie Nettles
Coralie has taught university physics and tutored high school and college students in STEM since 2012. She has a bachelor of science and doctorate in physics from the University of Newcastle. She has worked at universities and schools in three different countries.
View bio - InstructorAmanda Robb
Amanda has taught high school science for over 10 years. She has a Master's Degree in Cellular and Molecular Physiology from Tufts Medical School and a Master's of Teaching from Simmons College. She is also certified in secondary special education, biology, and physics in Massachusetts.
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Learn what trends are in data. Discover types of trends, patterns, and relationships in science and identify uses and limitations of trends in data science.Updated: 11/21/2023
Frequently Asked Questions
How do you explain data trends?
A hypothesis is a proposed explanation for observed data trends. If the hypothesis is testable, it can be used to support or refute data trends.
What is a trend in data analysis?
A trend in data analysis describes the general change in a variable with time. An example of a trend is the gradual increase in the Earth's average temperature with time due to climate change.
What is a trend pattern?
A trend pattern is the general change in a variable over time. They can be used to support or refute scientific hypotheses.
What are different types of trends?
There are three main types of trends. Increasing and decreasing trends describes general movement upwards and downwards respectively. A sideways trend describes horizontal movement.
Table of Contents
- What Is a Trend in Science?
- What Are Types of Trends in Data?
- What Are Types of Trend Patterns?
- Relationships in Trend Patterns
- Identifying Trends in Science
- What Are the Limitations of Trends?
- Applications of Trend Patterns
- Lesson Summary
Scientific data is the backbone of science. All scientific knowledge is based on trends, patterns and relationships in data. So, what is a trend in science? A trend describes the general direction of change of some variable. For example, the diagram shows the change in the Dow Jones Global Titans 50 Index over ten years. While there is a significant amount of variability over that time, the trend shows that it is increasing.
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Data doesn't always show trends. Sometimes there is no general change over time. Instead, data sometimes repeats in predictable ways. This repetition is known as a pattern. One example of this would be looking at the change in temperature with the seasons. Plotting the temperature with time over several years would show a predictable repeating pattern of higher temperatures in the summer months and lower temperatures in the winter months. Finally, relationships describe how one variable changes in response to another variable.
Trends, patterns and relationships are all incredibly important in data science. Scientific discoveries are generally made by first observing some phenomenon. A possible explanation for the observed phenomenon is then proposed. This is known as a hypothesis. A scientific hypothesis must be testable, meaning it must be possible to decide whether it is true based on experimentation.
A hypothesis is tested by collecting experimental data. Experiments are carefully designed when testing hypotheses, such that the information gathered provides the necessary information. This data is then analyzed to find trends, patterns and relationships, which are compared to predictions made by the hypothesis. If the data and the predictions match in a statistically significant way, the hypothesis is accepted.
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Trends were mentioned briefly above. But what are trends in data? They describe the general change in some variable in relation to another. The example given above was the value of a stock over ten years. Even though the value of the stock showed significant variability over that time, the general change was positive. This means that it was an increasing trend, or the trend was up.
Trends can also be described as decreasing, going down, or sideways. A decreasing trend means that the value of the variable is decreasing. An example of a down trend might be the population of an endangered species with time. A sideways trend describes the situation where the variable may increase or decrease by small amounts but there is no net or general movement. Prices often exhibit sideways trends in response to supply and demand. Each of these trends is illustrated in the diagram.
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Trends can be classified by the time over which they apply. They are generally categorized as short, medium, and long-term trends. Short-term trends are general changes that occur over hours or days. Long-term trends over years or decades, or even longer. Medium-term trends are somewhere in the middle. For example, looking at trends in the temperature. A short-term trend in temperature could be the increase in temperature from the morning to the afternoon. A medium-term trend could be the change in temperature between winter and summer. A long-term trend could be the increase in temperature over a decade due to climate change.
Trends can also be classified by how one variable changes with another. A linear trend is one where the variable changes by a fixed amount. The trend can be described by a straight line. An exponential trend describes when the variable changes by a smaller or larger amount each time. The trend can be shown with a curved line. A damped trend is one which shows some exponential change initially, but after some time changes to a linear trend or stops changing completely.
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Patterns describe when data repeats in a predictable way. As mentioned above, one example of this is the change in temperature with seasons. The temperature predictably goes up and down with each season. The temperature value is the lowest, also called a trough, in the middle of winter. This is a bottoming trend pattern. The temperature reaches a peak in the summer, a topping trend pattern. A continuation trend pattern may occur in the middle of trends, which could indicate that the trend will change or continue after some time.
A reversal pattern describes when the trend changes from direct to indirect or increasing to decreasing. Seasonality is a periodic and generally predictable pattern that repeats over a one-year period. Irregular patterns are changes that do not follow any kind of regularity. They are changes that are influenced by unexplained factors.
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Relationships can be direct, where increasing one variable increases the other. An example of a direct relationship is the current and voltage across a resistor. According to Ohm's law, {eq}V=IR {/eq}, when the current increases so does the voltage. They can also be indirect, where one variable increasing causes another to decrease. This is also known as an inverse relationship. Looking at Ohm's law, the relationship between current and resistance is an indirect relationship. For the same voltage value, an increased current means a decreased resistance. Examples of each type of relationship are shown in the diagram.
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After understanding trends, patterns, and relationships, the question is how dothese relate to scientific hypotheses? After collecting experimental data, analysis is used to identify any trends, patterns, or relationships within the data. These are then compared with predictions made by the hypothesis. If the data match the predictions in a statistically significant way, it supports the hypothesis. If the data does not match, it does not support the hypothesis. This means that the data refutes the hypothesis. In this case, it is necessary to alter the hypothesis and try again.
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There are many tools that are available in trend definition science. Some common tools include:
- Autocorrelation analysis is a tool for finding repeating patterns in data. It is used heavily in statistics and finance applications.
- Curve fitting describes the process of fitting a curved trend line to a series of data points. This can be used to determine if the trend is linear, exponential, or damped.
- Smoothing describes removing noise from a data set in order to better observe any trends or patterns. This is particularly important in applications such as economic analysis where many small changes can occur.
- Statistical tests, such as KPSS and MANCOVA, are used to determine if experimental data matches hypotheses.
- Graphing is a powerful tool for identifying trends, patterns, and relationships. It allows data to be visualized to observe features such as pennants, flags, and wedges in the data which can be used to make future predictions.
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Data is incredibly important in science. However, there are several limitations to using trend analysis. The first of these is that data may contain distortions. These are trends or patterns that are short-term or influenced by other factors. These can distort the overall findings and give incorrect results. It can also be difficult to determine the cause. If an unexpected trend or pattern is observed, it may not be possible to draw conclusions about the phenomenon.
Another limitation is sampling error: if the sample data is not fully representative of the entire phenomenon. As a result, the observations and conclusions are not representative. Furthermore, if the sample size is not large enough, the same problem can occur.
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As mentioned, analysis of trend patterns is an integral part of scientific discoveries. However, it is also used in many other applications. It is heavily used in finance and business analysis to predict the movement of prices of stocks and products. It is used in the fashion industry to predict shopping habits and what will be popular among consumers. It is also used in statistics to identify patterns in a large amount of data.
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Scientific data can be used to make discoveries about natural phenomena. When a phenomenon is observed, a scientific hypothesis is developed to explain the observations. Scientific data is collected and analyzed to find trends, patterns, and relationships.
- A trend is a general change in one variable compared to another over a period of time, such as a stock price increasing over the year.
- A pattern describes when a variable changes in a repeating or predictable way, such as the temperature over several seasons.
- A relationship describes how one variable changes in relation to another variable, such as voltage increasing with current. A direct relationship is when one variable increases with another. An indirect relationship is when one variable decreases as another increases.
The most important thing about a hypothesis is that it is testable. Experimental data is collected to support or refute scientific hypotheses. If the trends, patterns, and relationships within the data do not support a hypothesis, it is necessary to create a different hypothesis which can then be tested.
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Video Transcript
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.
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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.
From your data, you conclude that for every 1kg of mass added, the force measured in Newtons increases by 10. This is called a direct relationship: when one variable increases or decreases, the other variable does the same. The data fits the formula for Newton's second law: force is equal to mass multiplied by acceleration.
F = ma
Sometimes one variable has the opposite effect on the other. When studying motion, you measure how much time it takes for carts moving at different speeds to travel one meter. When you use a greater velocity, it takes less time for the car to travel the distance. This is called an indirect relationship: when one variable goes up the other goes down.
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Support or Refute?
So now that we can identify trends, patterns, and relationships during our data analysis, what do we do with that information? After any scientific investigation, you need to return to your hypothesis. First, refresh your memory on what you thought would happen. Next, go to your data and any trends, patterns, or relationships you found. Do these match what you predicted? Were there any discrepancies?
If your data matched your hypothesis, your hypothesis was supported. You can go on to repeat the experiment to improve the reliability of your results or proceed forward with further questions on the topic. However, sometimes our data refutes our hypothesis, meaning it does not match what we predicted. What do you do in this situation?
First, check again for trends. It might seem that your data doesn't fit your hypothesis, but look at the overall direction of your data. If it definitely doesn't match, think about another explanation of what you're seeing. If your data didn't support your hypothesis, it doesn't mean your experiment is wrong or bad; all it means is there is another scientific phenomenon happening, and it's your job to design a new experiment to find out what it is.
Lesson Summary
Scientific data is gathered from carefully designed experiments based on background research and a hypothesis. After data is collected, it can be analyzed by looking for trends, patterns, and relationships. Trends are general directions of data, such as an overall increase in global temperature. Patterns don't necessarily involve data going one way or the other, but rather describe a repeating observation. Relationships are like trends but involve a mathematical relationship, such as force and mass based on Newton's second law. Relationships can be direct, where both variables increase or decrease; or indirect, where one variable increases as the other decreases.
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