Tools of Environmental Science

Chapter 2

The nature of science• Science:– A systematic process for learning about the

world and testing our understanding of it– A dynamic process of observation, testing,

and discovery– The accumulated body of knowledge that

results from this process

• Science is essential – To sort fact from fiction – Develop solutions to the problems we face

Applications of science

Restoration of forest ecosystems altered by human suppression of fire

Policy decisions and management practices

Energy-efficient methanol-powered fuel cell car from DaimlerChrysler

Technology

• A technique for testing ideas with observations

• Assumptions:– The universe works

according to unchanging natural laws

– Events arise from causes, and cause other events

–We use our senses and reason to understand nature’s laws

The scientific method

The scientific method• A scientist makes an observation and asks questions of some phenomenon• The scientist formulates a hypothesis, a statement that attempts to explain the scientific question.• The hypothesis is used to generate predictions, which are specific statements that can be directly and unequivocally tested.• The test results either support or reject the hypothesis

Experiments test the validity of a hypothesis

Manipulative experiments yield the strongest evidence

(But, lots of things can’t be manipulated, experimented

with)

Natural or correlational tests show real-world complexity

(Results are not so neat and clean, so answers aren’t simply black and white)

The scientific process is part of a larger process• The scientific process includes peer review, publication, and debate

•A consistently supported hypothesis becomes a theory, a well-tested and widely accepted explanation

• With enough data, a paradigm shift – a change in the dominant view – can occur

STRENGTH: Science will change if evidence is there!!

Scientific Data

• Scientists collect a lot of data

• To draw conclusions and make informed decisions – Data needs to be organized and analyzed – e.g. graph data and look for trends

• Scientists rely on and use statistics to summarize, characterize, analyze, and compare data.

Statistics• A branch of Mathematics

• A collection of methods to analyze, categorize and compare numerical data

• Give an example of statistics you have heard of in your life

How and when we use statistics -examples

• Hypothesis: The average 10th grader is much taller than the average 7th grader:– test hypothesis by asking all students to write

down their height (you could also measure them), group responses into two groups – 7th and 10th grade

– problem: you have at least three 7th graders who are taller than one of the 10th graders

– solution: find the average height for 7th and 10th grade and then compare

Average or Mean

• Statistical populations are composed of similar individuals, but these individuals often have different characteristics (height of BHS 10th graders).

• A mean is the number obtained by adding up the data for a given characteristic and dividing this sum by the number of individuals.

• The mean is a single numerical measure for a population and allows for easy comparison.

Mean Examples

• Today I ate 4 Snickers, but really I don't eat that much – on average I eat 1 snickers/day

• Batting average (and other sport stat's)

MLB statisticsBatting average .367Hits

4,191Home runs

117Runs batted in

1,938Stolen bases

892

Distribution

• Remember that 10th grader that was not as tall as the rest of his classmates, there were several 7th graders taller than him; then there were the majority of 10th graders which were a head taller than the 7th graders, and even a few that were a head and a half taller than 7th graders – this is called a distribution – the way the individual data compares to the average

Distribution is the relative arrangement of the members of a statistical population, and is usually shown in a graph.

average – 1 head taller

1 ½ head tallerthe really short 10th graders

A bell shaped curve indicates a normal distribution where the data is grouped symmetrically around the mean.

Probability

• Probability - is the measure of the likeliness that an event will occur.

• Probability is quantified as a number between 0 and 1 (where 0 indicates impossibility and 1 indicates certainty).

• The higher the probability of an event, the more certain we are that the event will occur.

Probability vs. Chance

• Chance - a random occurrence – toss a coin 10 times – record how many times you had tails

• Sample size – toss a coin 1000 times - ? times did you get tails

Sample size• It is not practical to toss the coin 1000 times or

repeat your measurement/experiment 1000 times

• Statistical methods will help you decide whether you got your result by chance or whether it is meaningful – suppose you toss the coin 100 times and get tails 70 times – is it by chance or is something wrong with the coin (the way it was manufactured – it favors tails)

Probability vs. Chance

Another way to say this - the probability for getting tails is 0.5

You get a 0.7 – is this pure chance OR does it mean that the probability for this coin is NOT 0.5 (meaning there is something 'wrong' with this coin)

Statistics can help you figure out whether the difference between expected (0.5) and observed (0.7) is real or just chance, bad luck

Understanding Risk• Risk – the probability of an unwanted

outcome

• Some environmentalists worry about Oil spills – remember the big ones,BUT look at chart - much greater risk of oil pollutionfrom everyday sources.

Thinking About Risk• The most important risk we consider is the risk of

death.

• Most people overestimate the risk of dying from sensational causes, such as plane crashes, but underestimate the risk from common causes, such as smoking.

• Likewise, most people overestimate the risk of sensational environmental problems and underestimate the risk of ordinary ones.

Models

• Models are patterns, plans, representations, or descriptions designed to show the structure or workings of an object, system or concept.

• Scientists use several different types of models to help them learn about our environment.

Physical Models• 3D – you can touch them (model airplane;

model of the DNA molecule)

• Closely resemble object or system – might be scaled (< or >)

• Help further discoveries by helping to visualize, help in teaching – e.g. DNA structure model helped scientists uncover how DNA molecules duplicate themselves

Graphical Models

• Examples: Maps and Charts – very important in Environmental Science

• WHY? Give some examples.

Conceptual Models

Flow Charts;Graphic organizers

Mathematical Models

Mathematical models are one or more equations that represent the way system or process works.

Mathematical models are especially useful in cases with many variables, such as the many things that affect the weather.

Mathematical Models are used in weather forecasts.

Values and the Environment• Scientific research is an essential first step in solving

environmental problems.

• However, before research can begin, an examination of values is usually needed.

• Values are principles or standards that an individual considers to be important.

• There are many values that affect environmental decision making.

An Environmental Decision-Making Model

• A decision-making model is a conceptual model that provides a systematic process for making decisions.

• Decision-making models can be used to help you make decisions about environmental issues which can be very difficult.