Ok well heres what i've understood from the stuff that comes in Q2, hope it helps and please correct me anywhere if i am wrong!
STANDARD DEVIATION: amount of scatter about the mean. 95% results will lie within mean +2 where is STANDARD DEVIATION value.
STANDARD ERROR: should the mean be recalculated/sampling be redone, it tells us with 95% surity the new mean will lie within mean + 2[SM] where [SM] is our STANDARD ERROR value.
X2 test: Tells us whether our observed results are close enough to our expected results [I.E caused by CHANCE] the formula is:
[sum of] [observed - expected]2/expected.
we use DEGREES of freedom on the table according to the number of classes of data - 1 [number of classes of data can mean phenotypes, number of values, etc].
our base value for judgement is 0.05. value larger then the one for 0.05 means our test proves the NULL HYPOTHESIS IS WRONG, i.e, the results are significantly different.
The null hypothesis is basically saying the two samples are IDENTICAL with NO SIGNIFICANT DIFFERENCE between them in the beginning of the experiment.
STANDARD DEVIATION: amount of scatter about the mean. 95% results will lie within mean +2 where is STANDARD DEVIATION value.
STANDARD ERROR: should the mean be recalculated/sampling be redone, it tells us with 95% surity the new mean will lie within mean + 2[SM] where [SM] is our STANDARD ERROR value.
X2 test: Tells us whether our observed results are close enough to our expected results [I.E caused by CHANCE] the formula is:
[sum of] [observed - expected]2/expected.
we use DEGREES of freedom on the table according to the number of classes of data - 1 [number of classes of data can mean phenotypes, number of values, etc].
our base value for judgement is 0.05. value larger then the one for 0.05 means our test proves the NULL HYPOTHESIS IS WRONG, i.e, the results are significantly different.
The null hypothesis is basically saying the two samples are IDENTICAL with NO SIGNIFICANT DIFFERENCE between them in the beginning of the experiment.