1. Place the choice chamber on the bench.
2. Cover sectors with different layers of translucent material. Leave one sector with no cover.
3. Place a bench lamp directly over the choice chamber and turn it on.
4. Put 20 invertebrates into the choice chamber.
5. Start a stopwatch and leave for two minutes.
6. Record the number of invertebrates in each sector.
When testing the veracity of a proposed hypothesis, you use something called "statistical testing". To do this, you would first formulate two contradictory hypothesis. The first, called your null hypothesis, would assume that there was no variation between the number of invertebrates in areas of different light intensity. For example: your null hypothesis would be (# of invertebrates in light intense areas)=(#of invertebrates in areas of moderate light intensity). Next, you would construct your "alternative hypothesis", which would look like: (# of invertebrates in light intense areas) "is not equal to"(# invertebrates in areas of moderate light intensity). Once the null and alternative hyp. are determined, you would conduct research to collect data on the actual number of invertebrates in a light intense area, as well as the number of invertebrates in areas of low light. For instance, you might find that equatorial regions(high light) have 2000 bugs per square mile, while Canada(Low light/bad food) only has 500 per square mile. By plugging those numbers into your hypotheses, you will be able to determine which is true, the null or the alternate. If the alternate is true (which is the case with these example numbers), then the distribution is shown to depend upon light intensity. If the alternative hypothesis is false (i.e. Canada had just as many bugs as the Middle East), then your proposed hypothesis is most likely incorrect, and the distribution of invertebrates in a habitat is shown to be independent of light intensity. From here, you can use statistical analysis to determine the accuracy of your test, and the probability that what you concluded from the test is true(i.e. the test is true 95% of the time). This is the same as saying the probability that what you concluded is incorrect, and it was just a coincidence there were fewer bugs in the low light region, is 5%(called your "p-value"). P-values are included in the vast majority of scientific reports, and are commonly written as (p=.05). The more accurate the test, the smaller the p-value. I hope that is what you were looking for, good luck!
use a choice chamber, cover half with black paper and keep the other side clear and see which side has the most woodlice in.
The intensity of a line is proportional to the number of photons emitted or absorbed by the atoms. It depends on the number of atoms giving rise to the line.
A hypothesis for paper chromatography depends on what you are making the hypothesis on. A hypothesis for the speed of chromatography could be that you think the speed of the process can be changed depending on the type of paper, or whatever the stationary phase is, and the type of solvent being used.
That depends on the sound intesity level at the point of origen. Sound fall of with the square of the distance but the original intensity must be known.
Its volume or how loud it is.
it depends on how much the temp is.
get a baf
Petri dish Black marker Lamp Invertabrate
certain invertebrates are highly sensitive to light. light often means heat and some don't have a high tolerance of heat.
It depends on what you want to test. Goodnesss of fit or some null hypothesis?
The answer depends on what population characteristic A measures: whether it is mean, variance, standard deviation, proportion etc. It also depends on the sampling distribution of A.
A test statistic is a value calculated from a set of observations. A critical value depends on a null hypothesis about the distribution of the variable and the degree of certainty required from the test. Given a null hypothesis it may be possible to calculate the distribution of the test statistic. Then, given an alternative hypothesis, it is may be possible to calculate the probability of the test statistic taking the observed (or more extreme) value under the null hypothesis and the alternative. Finally, you need the degree of certainty required from the test and this will determine the value such that if the test statistic is more extreme than the critical value, it is unlikely that the observations are consistent with the hypothesis so it must be rejected in favour of the alternative hypothesis. It may not always be possible to calculate the distribution function for the variable.
It depends on what substance/object you are trying to determine the intensity.
depends if they have a backbone or not, if they do the no and if they don't then yes.
It depends entirely on what the hypothesis is.
The intensity of the basketball players helped them win the tournament.
It depends on you... there is no such rule to keep intensity at a perticular point...
Does it? Really?