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3 years ago

The resolving power of a microscope is described as the ability of the microscope to

1 answer:

4 0

The resolving power of a microscope is described as the ability of the microscope to differentiate between two nearby points.

Basically, a low resolution means that two dots close to one another will look like one blob, whilst a high resolution means that the two separate dots will be distinct.
So Ideally, you want a microscope with a high resolution.

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Which of these is measured during a scientific investigation? Group of answer choices Control group Test variable (independent v

Dovator [93]

Answer:

Outcome variable (dependent variable)

Explanation:

In a scientific investigation, a DEPENDENT VARIABLE (also known as outcome variable) is the variable that is being measured. The dependent variable, as the name implies, responds to the changes made to the independent variable.

For example, in an experiment where the effects of different concentration of fertilizer is used to test plant growth (height), the HEIGHT OF THE PLANTS is the dependent variable because it is the variable being measured.

5 0

3 years ago

Which of the following is a secondary color? A. blue B. orange C. red D. yellow

sineoko [7]

It depends on if you're talking about the secondary colors of light or paint, but the answer here that is logical is B. orange :3

6 0

3 years ago

How do the offspring plants compare to the parent plants?

rodikova [14]

They carry a combination of their genetic traits.

8 0

3 years ago

In what part of the cell does protein synthesis occur

Softa [21]

It occurs in the ribosomes.

6 0

3 years ago

Write a paragraph on testing probability

VashaNatasha [74]

Explanation:

Probabilities are described as ratios of favorable event outcome to the total number of event outcomes.

This is written as...

$P (E) =\frac{n(E)}{n(S)} \\$

where...

E= the number of times the event occurs

S= the number of trials

In biology experiments, hypotheses are formed based on research questions, and tested with the use of variables to provide a particular outcome. Statistics allows for testing data for consistency with the hypothesis, while statistical probability testing can be used in experiments to determine a range of outcomes, from genetic inheritance, evolutionary rates to theoretical experimental results.

In these statistical models, probability distributions are functions that give probabilities for certain event outcomes within an experiment (a set of trials). These may be either continuous, taking a value within a range of two numbers; or discrete, which may be either of two specified values. Discrete probability distributions list each value that a random variable may possibly take on.

Further Explanation:

For example, two types of probability distributions are employed in experimental biology:

Binomial distributions, which are discrete distributions, provide probability of a certain number of successful events for x a random variable, in a specific number of trials, n; here, the probability of success of an individual trial is constant at P and only one of two outcomes are possible- this is sampling with replacement.

where...

b(x; n, P)-the probability that an experiment of n trials results in x successes

nCx- the number of combinations of n things at r time

$b(x; n, P) = [ nCx ]* P^{x} * (1-P)^{n-x}\\$

This is often used in determining potential outcomes before data collection.

A type of continuous distribution, the student's t-test, compares standard deviations and means from two sets of samples or groups to check for significant differences between them.

$t= \frac{(x_{1} - x_{2}) }{\sqrt{(\frac{(S_{1}) ^{2} }{n1} }+ (\frac{(S_{2}) ^{2} }{n2 }}$

where...

x1 and s1 are the mean and standard deviation of sample 1 respectively
x2 and s2 are the mean and standard deviation of sample 1 respectively
n1 and n2 are sample sizes in samples 1 and 2 respectively

The null and alternate hypotheses typically theorize the likelihood and significance of certain event outcome probabilities. Critical values of t, along with degrees of freedom are used to determine a range of probable outcomes; probability or p- values along with this range, are used to determine whether either hypothesis is rejected or accepted.

For instance, significant differences between an experimental control and a specific treatment group would show that these occurrences are not due to sampling errors or random chance...

Learn more about calculating probability at brainly.com/question/4021035

Learn more about calculating event probability at brainly.com/question/6649771

#LearnWithBrainly

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3 years ago