Science Experiments

A world war. The election of many truly international politicians in hundreds of different countries. The slow changing of the global environment. The depletion of world resources and the invention of the Atomic bomb. Man made it into space, and onto the moon. Billions died, and billions were born, some died of old age, many didn't. The formation of the U.N, and the E.U. The eventual rise of the Asian states, along with a steady increase in global domestic product. A new millenia has passed, and numerous apocalyptic conspiracy theories have been debunked. The rise of the personal computer and of the internet. Eventually the arrival of WikiAnswers, and the formation of this rather generalised question; and a billion other events.

• Floods may occur infrequently. For this reason settlers may occupy flood plains inadvertently.
• Some flood plains have rich soils. The most famous of these, at least in the West, is the Egyptian flood zone.

An experiment.

Ecology. However, this is an incredible generalization - Ecology is more complex than the simple definition provided.

Lots.

answer: The Answer Is Renewablee :)

Seed germination is negatively affected by acid rain because of its (low) pH level and its direct chemical direct effects on the seed.

Acid rain is a term to describe precipitation containing contaminants that alter the natural pH of the rain (normally a pH of 7).

Contaminants can take the form of carbonic acid (HCO3) and has a pH that can vary between 5.8 and almost 7; a weak acid by most standards. Acid rain can also contain other "acidifying" elements such as Sulfur and Nitrogen which are more harmful.

Acid rain can cause direct damage to exposed foliage (seeds included) as well as a longer term effects to the soil where concentrations can build up over time.

This adds to the credibility of the original findings. People do make inadvertent errors, and sometimes unexpected variables are left uncontrolled. There are rare but important examples of researchers just out-and-out faking data, sometimes because they are simply dishonest, and sometimes because they are so totally convinced that they are right (only to be exposed and humiliated, usually ending careers). There is also the nature of information itself, and its analysis from a statistical point of view. Sometimes, for no reason other than "the fall of the dice", a result may survive a statistical analysis as significant, when it is not. [a false positive] This has nothing whatever to do with dishonesty or bad research technique. When statistical models are used to analyze data, the concept usually is to compare the real experimental data against theoretical models that are built on all possible outcomes assuming that there is no experimental effect whatsoever. A Little Added Detail: In other words, say that you get a certain result on a statistical analysis. You go to your tables and you see that results just like yours do come up by chance alone, but only one time in a thousand. This "one time in a thousand" comes from the fact that the theoretical model for the test you are using represents thousands or tens of thousands (or more) of theoretical results based on completely random data. So when you see that your results are similar to the "one time in a thousand" frequency in the model, you have considerable confidence that your results represent a real effect, and not the rather unlikely "one in a thousand" outcome. Confidence never actually reaches absolutely 100%, But we can (and should) set the desired confidence level even before we gather one bit of data, and can then carry on with our work.

The run the experiments over and over again, looking for anything that may have gone wrong to give them that outcome. They also test variables to see how changing them affects the outcome.

Also, research teams that are independent from the original team usually try to verify the outcome to verify the integrity of the experiment.

an ovaersold movie because ther are too many people fro the seats available

A solid homogeneous mixture is a combination of multiple substances that appear uniform throughout, with the same composition and properties in all parts. Examples include alloys like brass and solutions like saltwater.

the blood cycle is a phase that goes on in your heart to pump blood to your heart to keep you alive and your organs. the heart uses oxygenated and de-oxygenated blood to survive.

Controlled (or control) variables are things that are kept the same during an experiment. There are usually many- for example, when testing how light intensity affects photosynthesis, the controlled variable would be the type of plant used, or the color of the light.

Aluminum foil because that is the only substance that can hold more than 200,000,000,000,000,000,000,000 space particles and an egg dropped from a certain height can absorb 5 space particles less than the Aluminum F.. In the end you get an uncracked egg.

yes,it takes the good particles out of the air so you cant breathe

Soil quality affects humans in so many ways!

Think about what we use soil for... food!

If you compare agricultural high quality soil to low quality soil of deserts, much more can grow under high quality environments.

Also think about the interconnectedness of ecosystems.

For example take a mouse that is a herbivore and only feeds on plants. That mouse is then takes part in a broader food chain. Everything in an ecosystem depends on each other. Upper levels of an ecosystem will not be able to be as large or developed if the first link in the food chain (the photosynthesizers- plants!). For example if the soil is not quality (low in nutrients necessary for plant growth- ** Nitrogen, Phosphorus) then fewer plants will be able to grow. With fewer plants present, fewer animals can be supported and then there will be fewer predators of these animals. Humans are more on the top of the food chain so changes lower on the food chain will trickle up and impact us! Everything is connected.

Louis pasture

Alexander flemming

If you are using statistical analysis (and you are using it honestly) then it is understood that there is a small but finite possibility (usually 5%) that you obtained your significant results by chance alone. So replication of studies helps to support findings. There are methods of meta-analysis where results of many studies are examined together so that the pooled probabilities can be calculated with some degree of accuracy, even though there may be some complications of interpretation because different studies will have widely different hypotheses.

Silicon (periodic table) or the International System of Units.

Things that would cause a stream to move faster would be how large the stream is, how much material is in the stream and how much of a slope the stream is at. Things blocking the stream would also cause it to move slower.

Thunder is caused by the rapid expansion and contraction of air surrounding a lightning bolt. When lightning strikes, it superheats the air, causing it to rapidly expand and create a shockwave that we hear as thunder.

You will try to control many extraneous variables in any experiment.

Many psychology and medical experiments will have a "Control Group" that has some kind of sham or placebo test, and an "Experimental Group" that receives the treatment you are testing. In medicine, if necessary the control group will receive the "standard of care".

So, for example, if you wished to determine whether Dramamine prevented motion sickness, you could take 100 people that don't go out to sea frequently. Give half of them a dose of Dramamine, and give half of them a sugar cube (making them all believe they had the same "active" medication). Send them out deep sea fishing and observe complaints of nausea, or actual vomiting attacks. Use statistics to determine whether the sugar cube group (control group) got different results than the Dramamine group (experimental group).

Control groups can be vital for analyzing epidemiological data. However, since the experimenter isn't personally manipulating the data, a lot of care must be taken to verify that a representative control group is chosen.

For example, say you choose to "smoking" as your experimental variable, and you wish to study divorce rate among smokers compared to non-smokers. You could not ethically take a group of people and require half of them to smoke, and half of them not to smoke. You would have to take a group of people, some of which smoke, and some who don't smoke, and then either follow them through the future, or study historical data about them to determine trends. However, you will quickly find there are many confounding variables such as socio-economic status which could affect the observed variable (divorce rate). So, you might look at only doctors in similar health fields. Or only engineers, etc to eliminate as many confounding variables as possible.