Lab 2 Flashcards
alkali trapping method
is used to determine the amount of CO2 generated during soil respiration takes advantage of the fact that CO2 is weakly acidic.
When CO2 is absorbed by a basic solution, such as sodium hydroxide (NaOH), carbonate ions (CO3^-2 ) are formed:
CO2 + 2 NaOH → Na2 CO3 + H2O
The carbonate can be precipitated as barium carbonate (BaCO3 ) by the addition of an excess amount of barium chloride (BaCl2 ):
BaCl2 + Na2 CO3 → BaCO3 + 2 NaCl
We can then titrate the residual OH ¯ ions with hydrochloric acid (HCl) to determine how much of the initial base was not converted to carbonate, and by subtraction, calculate the amount of base that was used in the conversion to carbonate.
To calculate milligrams of CO2 evolved:
mg CO2 = ((ml base x N base) - (ml acid x N acid)) x equivalent weight of CO2.
In our case, we titrate 2.0 N NaOH with 1.0 N HCl, so to determine milligrams of CO2 dissolved in our 5 ml sample this equation becomes:
mg CO2 = ((5 ml x 2.0 N) - (ml HCL x 1.0 N)) x 22
To calculate total milligrams of CO 2 evolved per gram of soil/ litter per day, we need to multiply this amount by 5 to determine how much CO 2 was absorbed by our entire 25 ml volume of NaOH, divide by the weight of the soil/ litter sample, and divide by the number of incubation days:
mg Co2/g/day = mg CO2 x 5 / soil weight x 7 days
Why/when we use a t-test
we use a t -test if we want to test for differences between the means of two samples
Analysis of variance (ANOVA ) test
when you have collected data from three or more samples, Instead of using multiple t -tests to compare two samples at a time, a simpler and more statistically rigorous way of testing the null hypothesis that two or more samples are drawn from the same population is a ANVOA test.
test is mathematically more complex than a t -test.
determines the F statistic, which in this case is the ratio of the variation between a group of means relative to the variation within the groups:
F statistic
The ratio of the variation between a group of means relative to the variation within the groups
F = MSG / MSE
MSG (mean square the groups) is an estimate of the variance between groups.
MSE (mean square the error) is an estimate of the within -group variance.
since mean squares are calculated by dividing the sum of squares (the sum of the squared deviations) by the degrees of freedom, the value of F depends strongly on the degrees of freedom (so, sample size is important!).
the null hypothesis
for any statistical test is generally that there is no difference or relationship between groups or variables.
For ANOVA, the null hypothesis would be that there is no significant difference between the levels of the condition being tested.
if data supports Null, we would expect that the variances between groups and within groups would be similar.
If data does not support null and assuming we have appropriately sampled the data, the F ratio will be significant, as the variance between groups would be greater than the variance within groups.
alternative hypothesis for ANOVA
is that at least one of the means is different.
Post-Hoc Tests
Are used when a statistical test, such as ANOVA, has been performed, but additional information is needed to determine which means are significantly different from one another.
Differences are usually the result of simultaneous effects of multiple variables.
Tukey’s HSD test
is often used in conjunction with ANOVA. It is a single step multiple comparison procedure that compares all possible pairs of means and identifies any differences between two means that is greater than the standard error.
Tukey’s HSD test has two assumptions:
- Data is independent
- Variance is homogeneous
Statistical hypotheses for this test are:
H0 : µ1 = µ2 = µ3 = µn HA: µ1 ≠ µ2 ≠ µ3 ≠ µn
Basic equation calculating the test statistic (q-value) for the test is:
qs = Ya - Yb / SE
Where,
Ya = larger mean (of the two means being compared)
Yb = smaller mean (of the two means being compared)
SE = standard Error
If qs > qcritical, (a q -value obtained from the studentized range distribution), we can conclude that the two means being compared are significantly different from each other.
