33.3: Lab Report
Lab Experiment: Bean Beetles
Background information
Hypothesis and predictions
Materials and methods (include experimental design)
- Identify the independent variable(s).
- Identify the dependent variable(s).
- What variables will you keep standard?
- What is your control?
- Design data collection tables for both of your experiments on a separate sheet of paper.
For this data, it is most useful to perform a Chi Square analysis. Chi Square statistical test evaluate whether this is a significant difference between groups of data. The null hypothesis (H 0 ) is the hypothesis that states that there is no difference between the groups of data. The alternative hypothesis, (H a ) is the hypothesis you outlined above.
- Restate your H 0 hypothesis.
- H a hypothesis.
- If the null hypothesis is correct, what would we expect to see?
- If our alternative hypothesis is correct, what would we expect to see?
To do this statistical test, we need to calculate observed and expected values for the bean species and for our control. Our expected values are based on our null hypothesis (that there is no difference).
For example, if we had 40 eggs and four different treatment groups, we would expect there to be 10 eggs on each group.
| Treatment | Observed number of eggs | Expected number of eggs |
|---|---|---|
| Mung beans | 5 | 10 |
| Pinto beans | 25 | 10 |
| Chick peas | 10 | 10 |
| Control | 0 | 10 |
Fill in the data table below with our observed (what we actually found) and expected values.
| Treatment | Observed number of eggs | Expected number of eggs |
|---|---|---|
To calculate the chi-square value, or \(\chi ^{2}\), we simply add the square differences, divided by the expected, of all the observed and expected. In mathematical terms:
\(\chi ^{2} = \Sigma\frac{(O-E)^{2}}{E}\)
So for our example from the previous sample table, the first \(\frac{(O-E)^{2}}{E}\) would be \(\frac{(5-10)^{2}}{10}=2.5\)
We would then add to this value all of the other \(\frac{(O-E)^{2}}{E}\) in the table and then add to get the \(\chi ^{2}\) value.
| Treatment | Observed number of eggs | Expected number of eggs | Observed-Expected | (Observed-Expected) 2 | (Observed-Expected) 2 /Expected |
|---|---|---|---|---|---|
Compute the χ 2 value for your data by adding all of the (Observed-Expected)2/Expected values.
In order to find something to compare this number with, we need to calculate the degrees of freedom, or the number of different comparisons that can be made within the table. The degrees of freedom are the number of columns (M) minus one times the number of rows (N) minus one.
Degrees of freedom = (M − 1)(N − 1)
| A | B |
|---|---|
| C | D |
How many ways can this two by two table be broken into individual comparisons? Hint: Use the formula above.
Calculate the degrees of freedom in your experiment.
Now we can compare against the Chi distribution for the likelihood that our data is generated by chance. Remember, we are looking at the column labeled 0.05 for our p value. This means that at this level, we are 95% sure our results are real. The Chi distribution table gives us a critical value to compare our test value to.
- If test > critical value @ p level, reject null hypothesis
- If test < critical value @ alpha level, fail to reject null hypothesis
Compare to the Chi-distribution table(opens in new window) . Did your results come about by chance?
| DF/P | 0.995 | .990 | 0.975 | .950 | .900 | .750 | .500 | .250 | .100 | .050 | .025 | .010 | .005 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.00004 | .00016 | 0.001 | 0.004 | 0.016 | 0.102 | 0.455 | 1.323 | 2.706 | 3.841 | 5.024 | 6.635 | 7.879 |
| 2 | 0.010 | 0.020 | 0.0506 | 0.103 | 0.211 | 0.575 | 1.386 | 2.773 | 4.605 | 5.991 | 7.378 | 9.210 | 10.597 |
| 3 | 0.072 | 0.115 | 0.216 | 0.351 | 0.584 | 1.213 | 2.366 | 4.108 | 6.251 | 7.815 | 9.348 | 11.345 | 12.838 |
| 4 | 0.207 | 0.297 | 0.484 | 0.711 | 1.064 | 1.923 | 3.357 | 5.385 | 7.779 | 9.488 | 11.143 | 13.277 | 14.860 |
| 5 | 0.412 | 0.554 | 0.831 | 1.145 | 1.610 | 2.675 | 4.351 | 6.626 | 9.236 | 11.070 | 12.833 | 15.086 | 16.750 |
| 6 | 0.676 | 0.872 | 1.237 | 1.635 | 2.204 | 3.455 | 5.348 | 7.841 | 10.645 | 12.592 | 14.449 | 16.812 | 18.548 |
| 7 | 0.989 | 1.239 | 1.690 | 2.167 | 2.833 | 4.255 | 6.346 | 9.037 | 12.017 | 14.067 | 16.013 | 18.475 | 20.278 |
| 8 | 1.344 | 1.647 | 2.180 | 2.733 | 3.490 | 5.071 | 7.344 | 10.219 | 13.362 | 15.507 | 17.535 | 20.090 | 21.955 |
| 9 | 1.735 | 2.088 | 2.700 | 3.325 | 4.168 | 5.899 | 8.343 | 11.389 | 14.684 | 16.919 | 19.023 | 21.666 | 23.589 |
| 10 | 2.156 | 2.558 | 3.247 | 3.940 | 4.865 | 6.737 | 9.342 | 12.549 | 15.987 | 18.307 | 20.483 | 23.209 | 25.188 |
| 11 | 2.603 | 3.053 | 3.816 | 4.575 | 5.578 | 7.584 | 10.341 | 13.701 | 17.275 | 19.675 | 21.920 | 24.725 | 26.757 |
| 12 | 3.074 | 3.571 | 4.404 | 5.226 | 6.304 | 8.438 | 11.340 | 14.845 | 18.549 | 21.026 | 23.337 | 26.217 | 28.300 |
| 13 | 3.565 | 4.107 | 5.009 | 5.892 | 7.042 | 9.299 | 12.340 | 15.984 | 19.812 | 22.362 | 24.736 | 27.688 | 29.819 |
| 14 | 4.075 | 4.660 | 5.629 | 6.571 | 7.790 | 10.165 | 13.339 | 14.114 | 21.064 | 23.685 | 26.119 | 29.141 | 31.319 |
| 15 | 4.601 | 5.229 | 6.262 | 7.261 | 8.547 | 11.037 | 14.339 | 18.245 | 22.307 | 24.996 | 27.488 | 30.578 | 32.801 |
| 16 | 5.142 | 5.812 | 6.908 | 7.962 | 9.312 | 11.912 | 15.339 | 19.369 | 23.542 | 26.296 | 28.845 | 32.000 | 34.267 |
| 17 | 5.697 | 6.408 | 7.564 | 8.672 | 10.085 | 12.792 | 16.338 | 20.489 | 24.769 | 27.587 | 30.191 | 33.409 | 35.718 |
| 18 | 6.265 | 7.015 | 8.231 | 9.390 | 10.865 | 13.675 | 17.338 | 21.605 | 25.989 | 28.869 | 31.526 | 34.805 | 37.156 |
| 19 | 6.844 | 7.633 | 8.907 | 10.117 | 11.657 | 14.562 | 18.338 | 22.18 | 27.204 | 30.144 | 32.852 | 36.191 | 38.582 |
| 20 | 7.434 | 8.260 | 9.591 | 10.851 | 12.443 | 15.452 | 19.337 | 23.848 | 28.412 | 31.410 | 34.170 | 37.566 | 39.997 |
| 21 | 8.034 | 8.897 | 10.283 | 11.591 | 13.240 | 16.344 | 20.337 | 24.935 | 29.615 | 32.671 | 35.479 | 38.932 | 41.401 |
| 22 | 8.643 | 9.542 | 10.982 | 12.338 | 14.041 | 17.240 | 21.337 | 26.039 | 30.813 | 33.924 | 36.781 | 40.289 | 42.796 |
| 23 | 9.260 | 10.196 | 11.689 | 13.091 | 14.848 | 18.137 | 22.337 | 27.141 | 32.007 | 35.172 | 38.076 | 41.638 | 44.181 |
| 24 | 9.886 | 10.856 | 12.401 | 13.848 | 15.659 | 19.037 | 23.337 | 28.241 | 33.196 | 36.415 | 39.364 | 42.980 | 45.559 |
| 25 | 10.520 | 11.524 | 13.120 | 14.611 | 16.473 | 19.939 | 24.337 | 29.339 | 34.382 | 37.652 | 40.646 | 44.314 | 46.928 |
| 26 | 11.160 | 12.198 | 13.844 | 15.379 | 17.292 | 20.843 | 25.336 | 30.435 | 35.563 | 38.885 | 41.923 | 45.642 | 48.290 |
| 27 | 11.808 | 12.879 | 14.573 | 16.151 | 18.114 | 21.749 | 26.336 | 31.528 | 36.741 | 40.113 | 43.195 | 46.963 | 49.645 |
| 28 | 12.461 | 13.565 | 15.308 | 16.928 | 18.939 | 22.657 | 27.336 | 32.620 | 37.916 | 41.337 | 44.461 | 48.278 | 50.993 |
| 29 | 13.121 | 14.256 | 16.047 | 17.708 | 19.768 | 23.567 | 28.336 | 33.711 | 39.087 | 42.557 | 45.722 | 49.588 | 52.336 |
| 30 | 13.787 | 14.953 | 16.791 | 18.493 | 20.599 | 24.478 | 29.336 | 34.800 | 40.256 | 43.773 | 46.979 | 50.892 | 53.672 |
Conclusion:
- Write a paragraph restating your hypothesis and whether you accept or reject your hypothesis
- In this paragraph, explain why you accepted or rejected your hypothesis using data from the experiment . Include a brief summary of the data—averages, highest, lowest, etc., to help the reader understand your results and why you have come to particular conclusions.
- Discuss your thoughts about the possible reasons for your results (for example, if you chose salt water as a variable, give a possible reason why salt water, in particular, may have generated your results).
- Discuss possible errors that could have occurred in the collection of the data (experimental errors) and describe how these errors may have impacted the data.
Field Study: Campus Bird Ethology
Identify a species of bird at the pond. Write down its common and scientific name.
Watch an individual or group of individuals of the same species for ten minutes and list every behavior you see (examples: rest, peck object, dip head in water, social interaction).
Once you have your list, you will do two kinds of measurements for behavioral observations of your species.
-
Time budget: watch an individual of your species for 5 minutes, keep track of each behavioral change and how long the animal is doing that behavior.
- Calculate a percentage of time for each activity you saw. For example, if your bird spent 2 minutes and 10 seconds of the 5 minutes resting it would have spent 43% of its time resting. Convert the time to seconds, then divide time of activity by total time (in this example 130/300 = .4333, x100 for percentage = 43%).
- Total occurrences: watch an individual of your species for 10 minutes. Using the ethogram/list you made, put a tick mark next to the activity at every occurrence. If you individual was resting, then scratched its head, then went back to resting that would be two tick marks next to "rest" and one next to "scratched head". If you encounter a new behavior during this time add it to your list.
Which type of measurement do you think is better for measuring behaviors in a species? Why? What kinds of information might you want to study using time budget or total occurrence data?