Common examples of this would be if you place your hand in extremely warm or cold water, and an immediate response of pulling out of the water or any fluid, is an example of this extreme response, other responses are what is done in everyday, like being thirsty or hungry etc.
These behaviours are shared by all organisms even the most simple ones.
<h2><em>what is the main theme in reproduction.</em></h2>
- <em>The five central themes of biology are</em><em> </em><em><u>structure and function of cells, interactions between organisms, homeostasis, reproduction and genetics, and evolution</u></em>
<em>hope </em><em>it</em><em> helps</em>
<em>#</em><em>c</em><em>a</em><em>r</em><em>r</em><em>y</em><em> </em><em>on</em><em> learning</em>
<em>follow</em><em> me</em><em> and</em><em> mark</em><em> me</em><em> as</em><em> brainlist</em><em> plss</em>
Germ-line mutations are mutations that would be passed down to future generations, and recombinations are where the information each parent passes down to the offspring is shuffled.
The genetic variation would have to come from random events: False
Only alternate generations would express any genetic variable: False
Body cell mutations would be the only source of heritable genetic variation: False
There would be no new heritable genetic variation possible in the population: True
Imagine you are surveying a population of a mountain range where the inhabitants live in the valleys with no inhabitants on the large mountains between. If your sample area is the valleys, and you use this to estimate the population across the entire mountain range, <u>you overestimate the actual population size</u>
<u />
Explanation:
- An estimate that turns out to be incorrect will be an overestimate if the estimate exceeded the actual result, and an underestimate if the estimate fell short of the actual result.
- The mean of the sampling distribution of a statistic is sometimes referred to as the expected value of the statistic. Therefore the sample mean is an unbiased estimate of μ.
- Any given sample mean may underestimate or overestimate μ, but there is no systematic tendency for sample means to either under or overestimate μ.
- Bias is the tendency of a statistic to overestimate or underestimate a parameter. Bias can seep into your results for a slew of reasons including sampling or measurement errors, or unrepresentative samples
