1. Laboratory Skills and Statistics Topic 1
The Scientific Method
Laboratory Skills and Statistics
Topic 1

2. Laboratory Skills and Statistics
Topic 1 concerns Biological Statistics. To be successful in IB, it is essential that you have knowledge of statistics to apply and analyze data you collect during laboratory activities. To be successful on your Internal Assessments, I have put together this PowerPoint and some other materials that will be a useful resource for you.

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

4. 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)

5. 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:

6. 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:

7. 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

8. 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

9. 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

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

11. 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:

12. 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…

13. 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.

14. 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

15. 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

16. 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.

17. 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).

18. 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

19. 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

20. 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…

21. 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

22. Descriptive Statistics
Mean, median, and mode
Used to highlight trends or patterns in the data.
Frequency graphs are useful for indicating the distribution of data.
Standard deviation and standard error are used to quantify the amount of spread in the data and evaluate the reliability of estimates of the true mean.

23. Mean The average of all data entries
To calculate the mean…add up all the data entries, and divide by the total number of data entries.
When you DO NOT calculate a mean…
DO NOT calculate a mean from values that are already means themselves.
DO NOT calculate a mean of ratios for several groups of different sizes; go back to the raw values and recalculate.
DO NOT calculate a mean when the measurement scale is not linear; e.g. pH units are not measured on a linear scale.

24. Median The middle value when data entries are placed in rank order.
A good measure of central tendency for skewed distributions.
To calculate the median…
Arrange the data in increasing rank order.
Identify the middle value.
For an even number of entries, find the mid point of the tow middle values

25. Mode The most common data value.
Suitable for biomodal distributions and qualitative data.
To calculate the mode…
Identify the category with the highest number of data entries using a tally chart or a bar graph.

26. Range The difference between the smallest and largest data values.
Provides a crude indication of data spread.
To calculate the range…
Identify the smallest and largest values and find the difference between them

27. Standard Deviation
A frequently used measure of the variability (spread) in a set of data.
It is usually presented in the form of mean +/_ standard deviation.
For normally distributed data, about 68% of all values lies within +/- 1 standard deviation of the mean. This rises to about 95% for +/- 2 standard deviations.
Let’s practice…p. 40 (1-2)

28. T-test
Commonly used test when comparing two sample means, e.g. means for a treatment and a control in an experiment, or the means of some measured characteristic between two animal or plant populations.
A good test for distinguishing real but marginal differences between samples.
A two-group test, in other words, you must have only two sample means to compare.

29. Using T-tests
Used to determine if two populations or two groups are the same or not.
Null hypothesis: the two comparison groups are the same.
You compare the means (the average of all data entries) of the two groups such as small but distinguishing differences between the samples.

30. Using T-tests, ctd.. You must have only two sample means to compare.
You must assume that the data have normal distribution and the scatter of the data points is similar for both samples.

31. General steps in Student T-test
Step 1- calculate number of values (n), mean (x), and standard deviation (s)
Set up and state the null hypothesis.
Decide if you test is one or two tailed (can differ in one direction or both directions (+/-)
Calculate the t statistic (usually done by a spreadsheet)

32. General steps in Student T-test, ctd…
Determine the degrees of freedom (na + nb – 2)= df
Consult a t table to determine your P value (probability level)
Determine if your null hypothesis is accepted or rejected by comparing your calculated t value with the appropriate number of degrees of freedom to the value in your t chart. If you value is higher, your null hypothesis will be rejected.