The Scientific Method

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The Scientific Method

• Laboratory Skills and Statistics
• Topic 1

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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.

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The Scientific Method

• Making Observations
• Asking Questions
• Forming a Hypothesis
• Generating a Null Hypothesis
• Making Predictions
• Designing an Investigation
• Testing the Predictions
• Conclusion

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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)

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Hypotheseslets 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

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Hypothesesone 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

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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

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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

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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

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Controlled Variables

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

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Lets 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

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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!)
• Lets practice on page 21

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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.

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Types of Graphs

• Im sure youve 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

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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.
• Lets practice on p. 31

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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.

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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).

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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.
• Lets practice on pg. 32-34

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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.
• Lets practice on p. 27

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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).
• Lets practice on p. 28

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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.
• Lets practice on p. 29

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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.

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Mean

• The average of all data entries
• To calculate the meanadd 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.

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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

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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.

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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

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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.
• Lets practicep. 40 (1-2)

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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.

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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.

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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.

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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)

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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.