1. Practicing Science Table of Contents Math in Science Graphs Brainpop-
Measuring Matter
Precision & Accuracy

2. Math in Science Ch. 1. 4 What are some math skills used in science
Math in Science Ch What are some math skills used in science? What are some math tools used in science?

3. Vocabulary
Estimate- is an approximation of a number based on reasonable assumptions. Accuracy- refers to how close a measurement is to the true or accepted value. Precision- refers to how close a group of measurement are to each other. Significant figures- communicates how precise measurements are. Percent error- calculations to determine how accurate an experimental value is. Mean- the numerical average of a set of data. Median- is the middle number in a set of data- ex Mode- is the number that appears most often in a list of numbers. Ex. 10,14, 11, 10, 15, 13, 10, 11 Anomalous Data- Data that does not fit with the rest of a data.

4. What Math Skills do Scientist Use? Pg. 33
Math skills that scientists use to collect data include:
estimation, accuracy and precision, and significant figures.
An estimate is an approximation of a number based on reasonable assumptions. It is not a guess.
Scientists often rely on estimates when they cannot obtain exact numbers.
They may base an estimate on indirect measurements, calculations, models, or a sample
Do the Math!

5. What Math Skills Do Scientist Use? Pg. 34
In science, the words accuracy and precision have different meanings. Accuracy refers to how close a measurement is to the true or accepted value. Precision refers to how close a group of measurements are to each other. A reliable measurement is both accurate and precise.

6. Accuracy and Precision pg. 34
In science, the words accuracy and precision have different meanings.
Accuracy refers to how close a measurement is to the true or accepted value.
Precision refers to how close a group of measurements are to each other. A reliable measurement is both accurate and precise.

7. Accuracy and Precision
Fig. 1. Accuracy and Precision pg. 34
Accuracy and Precision
In a game of darts, accurate throws land close to the bull’s eye. Precise throws land close to one another. Where would darts land in the situations described on boards C and D?

8. Significant figures pg. 35
Significant figures communicate how precise measurements are.
The significant figures in a measurement include all digits measured exactly, plus one estimated digit.
When you add or subtract measurements, your answer can only have as many places after the decimal as the measurement with the fewest places after the decimal.
When you multiply measurements, the answers should only have the same number of significant figures as the measurement with the fewest significant figures.
ASSESS YOUR UND.
Pg. 35

9. Fig. 2. Adding or Subtracting Measurements pg.35
Significant Figures
Suppose you are tiling a bathroom. You might estimate that the tile is 5.3 cm long. The measurement 5.3 cm has two significant figures, or sig figs. You are certain of the 5, but you have estimated the 3.
Assess your Understanding pg. 35

10. Percent Error pg.36

11. Math Tools- Percent Error pg.36
The experimental density of copper is 9.37 g/cm3.
The true value is 8.92 g/cm3. To calculate the percent error, use the following formula and substitute.
The percent error in the calculation of the density of copper was 5.04%.

12. The mean is the numerical average of a set of data.
Mean, Median, Mode pg. 37
The mean is the numerical average of a set of data.
The median is the middle number in a set of data.
The mode is the number that appears most often in a list of numbers.
The range of a set of data is the difference between the greatest value and the least value in the set.
An important part of analyzing any set of data is to ask, “Are these data reasonable? Do they make sense?” Data that do not fit with the rest of a data set are anomalous data.

13. Mean, Median, Mode, and Range pg. 37
Fig. 3 Hognose Snake Egg Data
We can use math to analyze the data in the table about the number of hognose snake eggs in seven nests.
Fill in the boxes with the mean, median, mode, and range of the hognose snake egg data.

14. How and why are the number of sea turtle nests in Florida changing?
Reasonable and Anomalous Data pg. 38
An important part of analyzing any set of data is to ask, “Are these data reasonable? Do they make sense?”
r. pg.38
Data that do not fit with the rest of a data set are anomalous data.
Fig. 5
Think Like a Scientist
How and why are the number of sea turtle nests in Florida changing?

15. Reasonable and Anomalous Data pg. 38
Collected Data
What might be an unknown variable that could have affected data?
Fig. 5
An important part of analyzing any set of data is to ask, “Are these data reasonable? Do they make sense?”
r. pg.38
Data that do not fit with the rest of a data set are anomalous data.

16. Assess your Understanding pg. 39

17. CH. 1.4 Graphs What Kinds of Data DO Line Graphs Display?
Why are Line Graphs Powerful Tools?

18. To help see what data mean you can use a graph.
Line Graphs pg. 31
To help see what data mean you can use a graph.
A graph is a “picture” of your data.
One kind of graph is a line graph. Line graphs display data that show how one variable (the responding variable) changes in response to another variable (the manipulated variable).
Scientists control changes in the manipulated variable. Then they collect data about how the responding variable changes.
A line graph is used when a manipulated variable is continuous, which means there are other points between the tested ones.

19. Fig. 1 Graphs pg. 31 Analyzing Line Graphs
The line graph shows the results of an experiment that tested the amount of sugar that could dissolve in water as temperature was increased. What happens to the amount of sugar that can be dissolved in water when the temperature decreases?

20. Trends and Predictions Both kinds of line graphs are useful.
Fig. 2 Graphs pg. 32
Trends and Predictions
These graphs are fit from data. We can use these graphs to make predictions or identify trends.
A line graph in which the data points yield a straight line is a linear graph.
The kind of graph in which the data points do not fall along a straight line is called a nonlinear graph.
Both kinds of line graphs are useful.
How are they useful?
they allow you to identify trends, make predictions, and recognize anomalous data.
Graphs make it easy to see anomalous data points. When a graph does not have any clear trends, it probably means that the variables are not related.

21. U.S. Cell Phone Subscribers
Apply IT!! Pg. 33
U.S. Cell Phone Subscribers
Use the data in the table to describe a line graph.
Assess your Understanding

22. A hypothesis is a possible answer to a scientific question.
Doing Experiments pg. 34
Thinking and questioning is the start of a scientific inquiry process.
Scientific inquiry refers to the diverse (different) ways in which scientists study the natural world and propose (suggest) explanations based on the evidence they gather.
Scientific inquiry often begins with a 1.)question about an observation. In trying to answer a question, you are developing a 2.) hypothesis.
A hypothesis is a possible answer to a scientific question.

23. Developing a Hypothesis
Doing Experiments pg. 36
Developing a Hypothesis
A hypothesis is an educated guess about how things work. Most of the time a hypothesis is written like this: "If _____[I do this] _____, then _____[this]_____ will happen." (Fill in the blanks with the appropriate information from your own experiment.)
What are the two hypotheses that might answer this question: Why does it take the school bus longer to get to school on a Monday compared to a Friday?

24. How are Experiments Designed and Conducted? Pg. 37
After developing a hypothesis, you are ready to test it by designing an experiment. An experiment must follow sound scientific principles for its result to be valid. Variables are factors that can change in an experiment. The one variable that is purposely changed to test a hypothesis is the manipulated variable, or independent variable. The factor that may change in response to the manipulated variable is the responding variable, or dependent variable. All other variables must be the kept the same. An experiment in which only one variable is manipulated at a time is called a controlled experiment. In any experiment there is a risk of introducing bias.

25. Collecting and Interpreting Data pg. 39
Data are facts, figure, and other evidence gathered through qualitative and quantitative observations.
Collect data they need to be interpreted you can draw conclusions about your hypothesis.

26. Drawing Conclusions & Communicating pg. 40-41
A conclusion is a summary of what you have learned in an experiment. Communicating is the sharing of ideas and results with others through writing and speaking. Scientists communicate by giving talks at scientific meetings, exchanging information on the Internet, or publishing articles in scientific journals.

27. Fig. 5 Drawing Conclusions pg. 40
Sometimes the same experiment can produce very different data. If the data in this table were yours, what might you do next?

28. Do the Math pg. 39 Data Tables
A data table helps you organize the information collected in an experiment. Graphing the data may reveal whether there are patterns to your data. Do the data support the hypothesis that hummngbirds prefer red feeders?

29. What are Some Other Types of Scientific Observation? pg. 42
Sometimes it is not possible or desirable to design a controlled experiment to investigate a question. Different types of scientific investigations include observational research and opinion-based research. Observational research involves methods where the researchers try to observe an event without interfering. Opinion-based research involves asking people questions using surveys and interviews. The collected data is then analyzed.

30. Fig. 7 Observational Research pg. 42