You have data! What s next?


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1 You have data! What s next? Data Analysis, Your Research Questions, and Proposal Writing Zoo 511 Spring 2014
2 Part 1:! Research Questions
3 Part 1:! Research Questions Write down > 2 things you thought were interesting or engaging during the field trip (can be a species, a habitat feature, a relationship, etc). You can phrase these as questions, but you don t have to yet.
4 Part 1:! Research Questions What makes a good question?
5 Your questions should be specific and answerable NOT SO USEFUL What habitat do fish prefer? USEFUL Does sculpin CPUE differ among geomorphic units? In what kind of stream are brown trout most likely to be found? Is brown trout density related to flow velocity?
6 and statistically testable Does sculpin CPUE differ among geomorphic units? Is brown trout density related to flow velocity? 6 Sculpin CPUE Sculpin per minute Brown Trout/m 2 0 RIFFLE RUN POOL Current Velocity (m/s)
7 Part 2: Statistics How do we find the answer to our question?
8 Why use sta*s*cs? Are there more green sunfish in pools or runs? Pool Run ?? 10 Sta4s4cs help us find pa7erns in the face of varia4on, and draw inferences beyond our sample sites Sta4s4cs help us tell our story; they are not the story in themselves!
9 Statistics Vocab (take notes on your worksheet) Categorical Variable: Discrete groups, such as Type of Reach (Riffle, Run, Pool) Continuous Variable: Measurements along a continuum, such as Flow Velocity What type of variable is Mottled Sculpin /meter 2? What type of variable is Substrate Type?
10 Statistics Vocab Explanatory/Predictor Variable: Independent variable. On xaxis. The variable you use to predict another variable. Response Variable: Dependent variable. On yaxis. The variable that is hypothesized to depend on/be predicted by the explanatory variable.
11 Statistics Vocab Mean: The most likely value of a random variable or set of observations if data are normally distributed (the average) Variance: A measure of how far the observed values differ from the expected variables (Standard deviation is the square root of variance). Normal distribution: a symmetrical probability distribution described by a mean and variance. An assumption of many standard statistical tests. N~(µ 1,σ 1 ) N~(µ 1,σ 2 ) N~(µ 2,σ 2 )
12 Statistics Vocab Hypothesis Testing: In statistics, we are always testing a Null Hypothesis (H o ) against an alternate hypothesis (H a ). pvalue: The probability of observing our data or more extreme data assuming the null hypothesis is correct Statistical Significance: We reject the null hypothesis if the pvalue is below a set value (α), usually 0.05.
13 What test do you need? For our data, the response variable will probably be continuous. Ttest: A categorical explanatory variable with only 2 options. ANOVA: A categorical explanatory variable with >2 options. Regression: A continuous explanatory variable
14 Student s TTest Tests the statistical significance of the difference between means from two independent samples Null hypothesis: No difference between means.
15 Compares the means of 2 samples of a categorical variable p = 0.09 Mottled Sculpin/m 2 Cross Plains Salmo Pond
16 Analysis of Variance (ANOVA) Tests the statistical significance of the difference between means from two or more independent groups Mottled Sculpin/m 2 p = 0.03 Riffle Pool Run Null hypothesis: No difference between means
17 Precautions and Limitations Meet Assumptions Samples are independent Assumed equal variance (this assumption can be relaxed) Variance not equal sculpin density in pools sculpin density in runs
18 Precautions and Limitations Meet Assumptions Samples are independent Assumed equal variance (this assumption can be relaxed) Observations from data with a normal distribution (test with histogram)
19 Precautions and Limitations Meet Assumptions Samples are independent Assumed equal variance (this assumption can be relaxed) Observations from data with a normal distribution (test with histogram) No other sample biases
20 Simple Linear Regression Analyzes relationship between two continuous variables: predictor and response Null hypothesis: there is no relationship (slope=0)
21 Residuals Least squared line (regression line: y=mx+b)
22 Residuals Residuals are the distances from observed points to the bestfit line Residuals always sum to zero Regression chooses the bestfit line to minimize the sum of squareresiduals. It is called the Least Squares Line.
23 Precautions and Limitations Meet Assumptions Relationship is linear (not exponential, quadratic, etc) X is measured without error Y values are measured independently Normal distribution of residuals
24 Have we violated any assumptions?
25 Residual Plots Can Help Test Assumptions 0 0 Normal Scatter Fan Shape: Unequal Variance 0 Curve (linearity)
26 if assumptions are violated Try transforming data (log transformation, square root transformation) Most of these tests are robust to violations of assumptions of normality and equal variance (only be concerned if obvious problems exist) Diagnostics (residual plots, histograms) should NOT be reported in your paper. Stating that assumptions were tested is sufficient.
27 Precautions and Limitations Meet Assumptions Relationship is linear (not exponential, quadratic, etc) X is measured without error Y values are measured independently Normal distribution of residuals Interpret the pvalue and Rsquared value
28 Residuals
29 Pvalue: probability of observing your data (or more extreme data) if no relationship existed  Indicates the strength of the relationship, tells you if your slope (i.e. relationship) is nonzero (i.e. real) RSquared: indicates how much variance in the response variable is explained by the explanatory variable Does not indicate significance
30 RSquared and Pvalue High RSquared Low pvalue (significant relationship)
31 RSquared and Pvalue Low RSquared Low pvalue (significant relationship)
32 RSquared and Pvalue High RSquared High pvalue (NO significant relationship)
33 RSquared and Pvalue Low RSquared High pvalue (No significant relationship)
34 We just talked about: Types of variables 3 sta*s*cal tests: t test, ANOVA, linear regression When to use these tests How to interpret the test sta*s*cs How to be sure you re mee*ng assump*ons of the tests
35 Part 3: Proposal
36 Wri*ng a Proposal What is the func*on of a proposal? To get money
37 Wri*ng a Proposal What is the func*on of a proposal? What informa*on should go in a proposal? Research goals/objec3ves/hypotheses/ques3ons Why does this ma?er? (Ra3onale) Procedure / Methods Future direc*ons / implica*ons Budget/cost analysis Expected results
38 Other data you can use Previous years data on website: all of the same information was collected from the same place, around the same time of year. Replication! USGS: Background info: from the Upper Sugar River Watershed Association Think about these data sources as you generate your questions.
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