1. The Scientific Method Honors Biology Laboratory Skills

2. The Scientific Method Making Observations Asking Questions Forming a Hypothesis Generating a Null Hypothesis Making Predictions Designing an Investigation Testing the Predictions Conclusion

3. Hypotheses and Predictions Hypothesis- A scientific hypothesis is a possible explanation for an observation or a scientific problem that is given to you. Features of a sound hypothesis: It offers an explanation for an observation It refers to only one independent variable It is written as a statement and not a question It is testable by experimentation It is based on further research, observations or prior knowledge It leads to predictions about the system (or the topic of your experiment)

4. Hypotheses…let’s give it a try! Example 1: During an experiment on bacterial growth, the girls noticed that bacteria in cultures grew at different rates when the dishes were left overnight in different parts of the laboratory. (This is an observation) Hypothesis:

5. Hypotheses…one more try! Example 2: Observation – During an experiment on plant cloning, a scientist noticed that the root length of plant clones varied depending on the concentration of a hormone added to the agar. Hypothesis:

6. Variables When you are planning an investigation, you must identify the variables that you are testing and the ones that you keep constant. A variable is any characteristic or property able to take any one of a range of values. Independent variable Dependent variable Controlled variable

7. Independent Variable Set by the person carrying out the investigation (ex. Temperature, light intensity, pH) Recorded on the x axis of the graph during data presentation There is always only one in an investigation Must record proper unit

8. Dependent Variable Measured during the investigation (ex. Plant growth, heart rate etc) Recorded on the y axis of the graph during data presentation There is always only one in an investigation Must record proper unit

9. Controlled Variables Factors that are kept the same or controlled. List these in the method as appropriate to your own investigation

10. Let’s play with variables! Turn to page 18 in your Scientific Method packet. Look at the picture and explanation on catalase activity and answer the following questions: 1. Write a suitable hypothesis for this experiment: 2. Name the independent variable with the proper unit: ___________________________ 3. List the equipment needed to set the independent variable, describe how it was used: 4. Name the dependent variable with the proper unit: ___________________________ 5. List the equipment needed to set the dependent variable, describe how it was used: 6. List three variables that might have been controlled in this experiment:

11. Data Collection Design a data table to record your results. Your data table should clearly show the units and values of the independent and dependent variables. When you design your data table, leave some room for data processing. (IB likes to see your math!) Let’s practice on page 21…

12. Data Presentation Graphical presentation of data provides a visual image of trends in the data in a minimum of space. The following are a list of characteristics of a well-done graph: accurately shows the facts complements or demonstrates arguments presented in the text has a title and labels is simple and uncluttered shows data without altering the message of the data clearly shows any trends or differences in the data is visually accurate (i.e., if one chart value is 15 and another 30, then 30 should appear to be twice the size of 15) Constructing and reading graphs is one of the most basic standards of the Ohio State Science Curriculum. You must be able to do both.

13. Types of Graphs I’m sure you’ve learned all about graphs in Math class, but we are going to review them with respect to Biological Statistics. The most challenging part about graphing is deciding which graph to use. Choosing the wrong graph can obscure information and make data more difficult to interpret. Some examples… Scatter Graph Line Graph Bar Graph Histogram Pie Graph

14. Scatter Graph In scatter graphs, there is no manipulated (independent) variable but the variables are usually correlated. The points on the graph do not need to be connected, but a line of best fit is often drawn through the points The data for this graph must be continuous for both variables. Useful for determining the relationship between two variables. Let’s practice on p. 31

15. Line Graph Line graphs are used when one variable (called the independent variable), affects another, the dependent variable. The independent variable is often time or the experimental treatment. The dependent variable is usually the biological response. The data for line graphs must be continuous for both variables. If extreme points are likely to be important, draw a line connecting the points.

16. Line Graphs, continued… Error Bars! IB knocks off SIGNIFICANT points on graphs if you do not included error bars when necessary. Why do you need error bars, in other words, what do they tell you? Where error bars are large, the data are less consistent (more variable) than in cases where the error bars are small. When do you need error bars?: Error bars are used if there are calculated mean (average) values and a measure of data spread (standard deviation).

17. Line Graphs, continued… Two curves plotted together More than one curve can be plotted per set of axes. This is useful when you wish to compare two data sets together. If the two data sets use the same measurement units and a similar range of values use one scale and distinguish the two curves with a key. If the two data sets use different units and/or have a very different range of values use two scales Adjust scales if necessary to avoid overlapping plots. Let’s practice on pg. 32-34

18. Bar Graph The data for this graph are non-numerical and discrete for at least one variable, in other words, they are grouped into separate categories. There are no independent or dependent variables. Axes may be reversed to give graph with the categories on the x axis. The data are discontinuous, so the bars do not touch Data values may be entered on or above the bars Multiple data sets can be displayed using different colored bars placed side by side within the same category. Let’s practice on p. 27

19. Histograms Histograms are plots of continuous data, usually of some physical variable against frequency of occurrence. Column graphs are drawn to plot frequency distributions when the data are discrete, numerical values (1,2,3, etc). In this case, the bars do not touch. The X-axis usually records the class interval. The Y-axis usually records the number of individuals in each class interval (frequency). Let’s practice on p. 28…

20. Pie Graph As with bar graphs, pie graphs are used when the data for one variable are discrete (categories) and the data for the other are in the form of counts. A circle is divided according to the proportion of counts in each category. Pie graphs are: Good for visual impact and showing relative proportions. Useful for six or fewer categories. Not suitable for data sets with a very large number of categories. Let’s practice on p. 29