AP Biology Practice 5 - Analyze Data and Evaluate Evidence

Summary

This AP Biology practice video by Mr. Andersen focuses on analyzing and evaluating scientific data. It emphasizes the importance of recognizing patterns, outliers, and extraneous data, and uses Charles Keeling's atmospheric carbon dioxide data as an example. The video also covers how to interpret data in various biological contexts, including evolution, free energy, information processing, and systems. It concludes by highlighting the value of data in understanding complex phenomena, such as NASA's visualization of ocean currents.

Takeaways

• 📊 The importance of data analysis in science is emphasized, with a focus on identifying patterns, outliers, and extraneous data.
• 🌿 Charles David Keeling's work on atmospheric carbon dioxide levels at Mauna Loa, Hawaii, is highlighted as an example of significant data collection.
• 📈 Organizing data through graphs is a crucial step in making sense of overwhelming amounts of data and identifying trends.
• 🌡 The script discusses the impact of annual cycles on carbon dioxide levels, influenced by the sun's movement and plant growth.
• 🧪 The relationship between fertilizer amount and plant growth is presented as an example of how data visualization can reveal relationships and potential outcomes.
• 🔍 The need to control for variables and ask the right questions when analyzing data is stressed to ensure meaningful conclusions.
• 🌐 The script touches on the 'big ideas' in biology that the College Board may test, including evolution, free energy, information, and systems.
• 🔬 Examples of how to analyze data in the context of these big ideas are provided, such as the sucrose lab for free energy and signal transduction in information.
• 📝 The ability to write short essays explaining biological factors that determine graph shapes, like predator-prey relationships, is a skill that may be assessed.
• 🔍 Identifying possible sources of error in a dataset and understanding how to revise protocols for more valid data is an important aspect of scientific inquiry.
• ❓ Multiple choice questions and the ability to interpret genetic data, such as understanding epistasis in flower color inheritance, are part of evaluating data analysis skills.

Q & A

What is the primary focus of AP Biology Science Practice 5?

The primary focus of AP Biology Science Practice 5 is analyzing data and evaluating evidence, particularly looking at how to determine if the data collected is good or bad, identifying patterns, and understanding the implications of the data for the research question.

Why is organizing data important when analyzing it?

Organizing data is crucial because it helps to identify patterns, outliers, and trends that might not be apparent when looking at raw data. Visualization tools like graphs are especially useful in making sense of large data sets.

What was the significance of Charles David Keeling's data collection?

Charles David Keeling collected significant data on atmospheric carbon dioxide at Mauna Loa, Hawaii. His data revealed an increase in CO2 levels over time, which is closely linked to global warming and the greenhouse effect.

How can annual cycling affect atmospheric carbon dioxide levels?

Annual cycling affects atmospheric carbon dioxide levels due to the varying amounts of plant growth as the sun moves between the northern and southern hemispheres. This results in different levels of CO2 being absorbed and released.

What is a potential outcome of increasing fertilizer amounts on plant growth?

Increasing fertilizer amounts generally leads to an increase in plant growth, as visualized by a curve on a graph plotting fertilizer amount against plant growth. However, there may be a point of diminishing returns or negative effects if fertilizer is overused.

How might the College Board test your ability to analyze data?

The College Board might test your ability to analyze data by asking you to identify patterns, understand relationships like predator-prey dynamics, or recognize potential sources of error in experiments such as the potato cores in different sucrose solutions.

What are biotic factors, and how might they influence a predator-prey relationship?

Biotic factors include elements like food supply, space, competition with other organisms, and interactions between predator and prey. These factors influence predator-prey dynamics by affecting population sizes and the overall stability of the ecosystem.

What could be a potential source of error in the potato cores experiment?

A potential source of error in the potato cores experiment could be mislabeled beakers, leading to incorrect molarity readings and unexpected changes in mass that do not align with the expected outcomes.

What is epistasis, and how does it relate to the genetics question in the script?

Epistasis is a genetic phenomenon where one gene affects the expression of another gene, influencing traits like flower color. In the genetics question discussed in the script, epistasis accounts for the unexpected ratio of flower colors observed.

How does NASA use data visualization to aid in understanding complex data sets?

NASA uses data visualization to help make sense of complex data sets, such as ocean currents, by creating animations like 'Perpetual Ocean.' These visualizations make it easier to identify patterns and learn from the data, even when it is vast and intricate.

Outlines

00:00

📊 Analyzing Data and Evaluating Evidence in AP Biology

Mr. Andersen introduces the importance of analyzing data and evaluating evidence in AP Biology. He emphasizes the need to assess the quality of data, identify patterns, outliers, and extraneous data. The video references Charles David Keeling's atmospheric carbon dioxide data from Mauna Loa, Hawaii, and how it can be organized and graphed to reveal trends related to global warming. The summary also touches on the significance of understanding the relationship between fertilizer and plant growth, and how data analysis can inform scientific questions within the four big ideas of AP Biology: evolution, free energy, information, and systems.

05:01

🌐 Data Visualization and Its Applications in Science

This paragraph discusses the value of data visualization in understanding complex scientific phenomena. It uses the example of a genetics experiment involving flower color inheritance to illustrate how data can be analyzed to understand patterns and ratios. The video script guides viewers through a genetics problem, where the observed ratios in the F2 generation suggest epistasis, a genetic phenomenon where one gene influences the expression of another. The paragraph concludes with a mention of NASA's use of data visualization, specifically the 'Perpetual Ocean' animation, to make complex ocean current data accessible and informative.

Keywords

💡Data Analysis

Data analysis refers to the process of examining, cleaning, transforming, and modeling data to extract useful information, draw conclusions, and support decision-making. In the context of the video, data analysis is crucial for evaluating evidence and drawing conclusions about scientific phenomena. The script discusses the importance of organizing data and identifying patterns, outliers, and extraneous data to ensure the quality of the conclusions drawn.

💡Charles David Keeling

Charles David Keeling was an American geochemist known for his precise measurements of atmospheric carbon dioxide (CO2) levels at the Mauna Loa Observatory in Hawaii, which began in 1958. His work has been fundamental in understanding the increase in atmospheric CO2 and its relation to global warming. The script mentions Keeling as an example of a scientist who collected significant data over his lifetime, emphasizing the importance of data collection in scientific research.

💡Atmospheric Carbon Dioxide

Atmospheric carbon dioxide (CO2) is a greenhouse gas that plays a key role in global warming and climate change. The video script highlights Keeling's data on atmospheric CO2 levels, showing an increasing trend over the years, which is tied to the greenhouse effect and global warming. This concept is central to understanding one of the major environmental challenges of our time.

💡Global Warming

Global warming refers to the long-term increase in Earth's average surface temperature due mainly to human activities, such as the emission of greenhouse gases like carbon dioxide. In the script, the increase in atmospheric CO2 levels is directly linked to global warming, illustrating the impact of human activities on the planet's climate.

💡Annual Cycling

Annual cycling in the context of the video refers to the seasonal variations in atmospheric CO2 levels due to changes in plant growth influenced by the position of the sun between the northern and southern hemispheres. This natural phenomenon is an important factor to consider when analyzing data for trends and patterns, as it can affect the interpretation of results.

💡Fertilizer

Fertilizer is any material of natural or synthetic origin that is added to soil or water to supply nutrients essential for plant growth. In the script, the effect of fertilizer amount on plant growth is used as an example of how data can be graphed and analyzed to understand the relationship between the two variables, which is a fundamental concept in experimental design and data analysis.

💡Graph

A graph is a visual representation of data, showing the relationship between variables. In the video, graphs are used to organize and visualize data, such as the relationship between fertilizer amount and plant growth, making it easier to identify patterns, trends, and correlations.

💡Outliers

Outliers are data points that are significantly different from other observations, potentially indicating errors in data collection or representing rare events. The script mentions the importance of identifying outliers in data analysis to ensure the validity and reliability of the conclusions drawn from the data.

💡Control

In scientific experiments, control refers to a standard or baseline against which experimental conditions are compared. The script discusses the need to control for variables in data analysis, such as the annual cycling of CO2 levels, to accurately assess the impact of the experimental variables on the results.

💡Evolution

Evolution is the process by which different kinds of living organisms develop and diversify from earlier forms during the history of the Earth. The script mentions that the College Board may ask questions related to the history of our planet, which could involve understanding evolutionary processes and the data that supports them.

💡Signal Transduction

Signal transduction is the process by which a cell converts an external signal or stimulus into an internal response. In the context of the video, signal transduction is mentioned as an example of a biological process that can be analyzed through data collection and analysis, such as in the blood glucose feedback loop.

💡Systems

In biology, systems refer to the complex sets of components that work together to perform a particular function within an organism. The script uses the example of non-competitive inhibition of an enzyme to illustrate how the structure of a molecule can affect its function within a biological system, emphasizing the importance of understanding the interplay between structure and function.

💡NASA

The National Aeronautics and Space Administration (NASA) is a United States government agency responsible for the nation's civilian space program and for aeronautics and aerospace research. The script mentions NASA as an example of an entity that collects vast amounts of data, such as ocean currents, and the importance of visualizing this data to gain insights and understanding.

Highlights

The importance of analyzing data to determine the quality and relevance of collected information.

Charles David Keeling's extensive data collection on atmospheric carbon dioxide levels at Mauna Loa, Hawaii.

The necessity of organizing data through graphs to identify patterns and trends.

The correlation between increasing atmospheric carbon dioxide and global warming.

The annual cycling of carbon dioxide levels due to variations in plant growth influenced by the sun's position.

The method of graphing data with fertilizer amount on the x-axis and plant growth on the y-axis to observe relationships.

The potential for extrapolation from data to predict outcomes beyond the collected data set.

The College Board's focus on testing the ability to analyze data within the four big ideas of AP Biology.

The examination of historical data to understand evolutionary patterns on our planet.

The sucrose lab experiment and its relevance to understanding free energy and osmosis.

Signal transduction as a key concept in understanding how cells respond to external information.

The structure-function relationship in biological systems, exemplified by non-competitive enzyme inhibition.

Analyzing data to identify patterns as a key skill in AP Biology, demonstrated through short essay questions.

The use of data to explain biological factors determining the shape of graphs, such as in predator-prey relationships.

Identifying possible sources of error in a data set and proposing revisions to obtain more valid data.

The application of data analysis in multiple-choice questions, such as in genetics problems involving epistasis.

The role of data visualization in making complex data sets more accessible and understandable.

NASA's efforts in data collection and the challenges of effectively communicating that data to the public.

The 'Perpetual Ocean' animation as an example of innovative data visualization techniques.

Transcripts

Hi. It's Mr. Andersen and this is AP Biology science practice 5. It's on

analyzing data and evaluating evidence. Remember in the last two practices we talked you know,

good questioning and then good collection of data. But once you have a bunch of data

then you have to start looking through it and telling, is this good data or bad data?

Is there extraneous data? Are there outliers? How do I control for that? Then more importantly,

what does it tell me about my question? And one person who collected a lot of data during

his lifetime what Charles David Keeling. And he was collecting data on the amount of atmospheric

carbon dioxide at Mauna Loa Hawaii. And so this is just a sampling of some of his data.

And when you look at it, the first thing you might realize is, wow, this is overwhelming.

This is just data from one year. So I can't tell anything from that. And so the first

thing you want to do is you want to organize the data. And a graph is a great way to do

that. So what we're looking at is from 1960s until today, this is the amount of atmospheric

carbon dioxide. And so we can see that it's increasing and this is clearly tied to global

warming and the green house effect. You also see annual cycling that we would have to account

for. That has to do with the sun moving to the northern and southern hemisphere. And

so we get different amounts of plant growth. And therefore we get varying amounts of carbon

dioxide. And so the first step is looking at the data and seeing are there patterns

within this that I can learn from? But then we also want to control for that. And so let's

say I give you the following question. How does fertilizer amount effect plant growth?

And you collect a lot of data. Well looking at that data I don't learn much until I start

to graph it and take a look at it. So if we put fertilizer on the x and plant growth on

the y, now I see a relationship or a curve of fit that says an increase in fertilizer

is going to give me an increase in plant growth. What might happen after that? We could extrapolate

on what would happen if we increase it. But now we could look at the y. Why is the increase

in fertilizer going to increase the amount of plant growth. And so we can look at questions

like that. And so the college board is going to ask you or test your ability to analyze

data in each of the following four big ideas. And so if we're looking at evolution, they've

said that they could ask you questions related to the history on our planet. Now they're

not going to ask you a lot of minutia questions about learning all of the devonian, learning

all of the periods and eras and epics. But they could ask you sequential questions or

gathering data from a certain era, what does that tell us. In the area of free energy,

the sucrose lab is a great one they keep coming back to. So this is again taking potatoes.

Putting them in different concentrations of sucrose solution and then looking at what

happens to their percent change in mass. If we're looking at information, this is clearly

signal transduction. And so how cells are taking information outside and then responding

to that. So the whole blood glucose feedback would be a great example of questions they

could ask you. And then the area of systems, structure fits function. In other words, this

non competitive inhibition of an enzyme. And so how does the structure of that competitor

molecule, how's it going to effect it's function? And so let's look at some examples. And so

they're going to ask you questions in three different areas. And the first one is they

want you to be able to analyze data to identify patterns. And so this would be an example

of a short essay question they might ask. In one paragraph explain biological factors

that determine the shape of the graphs pictured above. And so this is clearly the perfect

example of the predator-prey relationship. And they're asking you to look at biotic factors.

And so why is the prey going to vary like this. And we could talk about you know the

food supplies, the amount of space that they have. Maybe it's competition with other organisms.

Other prey species. And then interactions with the predator. Why are we seeing an increase

in the predator species? Well we had an increase in prey. So now predators are going to have

more young, but then as the prey drops off the predators are going to drop off. And so

there's lots of areas that you could take this into using the data that you're presented

here. Let's say we give you straight out data set like this. So this is that potato lab

where you're going to put different potato cores in different concentrations of sugar

water. So they have different molarity here. This is the initial mass of the potato cores.

And then this is the final mass. So they might ask you to identify possible sources of error

in the data set. And so we've learned so well what happens if we have different concentrations

of sugar water, but maybe this data is wrong. So if we look at it right here, I see that

there's no change in the 0.4, but the when it's in 0.2 I'm seeing a decrease in that

mass. And that doesn't seem right. And so maybe the beakers were mislabeled. And so

how could we revise the protocol to obtain more valid data. And so be looking out for

that. Being able to take in observations and then refine that. Where is the problem coming

from and then trying to correct that. And sometimes the data is just going to be in

a multiple choice question. So right here we've got a genetics question where we have

these tiny blue eyed Mary flowers. We've got blue. But sometimes we'll have white and pink

it says in the description. So they're giving you the crosses, the p generations, the f1

and then the f2. And as I look through this I see this looks like a 3 to1, a 3 to1, and

this looks a little bit crazy down here. Almost like a 2 to 1 to 1. And so which of the following

accounts for that explanation? So you may want to pause the video and then take a stab

this question. As I went through it I was able to rule out, I mean it sure looks like

inheritance. I was able to cross out the first three and the right answer here is going to

be D. And so we're looking at is that there's another gene product. And so this is epistasis.

We're having one gene effecting other genes accounting for the different colors. And so

again data is amazing. We collect data. We first have to visualize it and then we try

to explain it. And it's not always easy to do that. Sometimes we have to look back at

our question. Was the question good? Was the controls good? But once we have data, data

is amazing. And in one entity in the states that collects a huge amount of data is NASA.

But they don't always know how to get that data back to the people. And so this is a

group at NASA that's helping them to visualize that. And they've created this animation called

the perpetual ocean which is looking at the ocean currents. And it almost looks that a

Van Gogh. But if we run it we can learn a ton from data. And I hope that was helpful.