Scientists

Louis Pasteur was born in Dole in the Jura region of France.

Observation is important in science because it allows for recordings during an experiment. It also is used as support for hypotheses.

Scientists use a variety of tools to study the universe, including telescopes (both on Earth and in space), satellites, and instruments that detect different forms of radiation such as radio waves, X-rays, and gamma rays. They also use computer simulations and mathematical models to analyze and interpret the vast amount of data collected from these observations.

Nicola Tesla died January 7 1943 at the age of 86. If he were alive today he would be 152 years old.

In laboratory tests, trace BPA exposure has been shown to disrupt the endocrine system and trigger a wide variety of disorders, including chromosomal and reproductive system abnormalities, impaired brain and neurological functions, cancer, cardiovascular system damage, adult-onset diabetes, early puberty, obesity and resistance to chemotherapy.

And it's in everyone, even newborn babies: Environmental Working Group's latest research found, for the first time, BPA in umbilical cord blood. Read the report, see the FAQ's and get tips on avoiding BPA exposure here: http://www.ewg.org/minoritycordblood

He died in his sleep for a heart failure at the New York hotel.

Bacteria are difficult to see when they are not stained because they are almost colorless.

1. Staining a slide enables a high contrast between the bacteria and the background.

2. It defines the bacterial morphology (size, shape and arrangement).

3. It enables us to observe structures like flagella, capsules and endospores.

Because there are no negative numbers

Many thermodynamic equations need absolute numbers. At 0 degrees Kelvin, molecules stop. It is absolute 0--it doesn't get colder. There is a direct correlation between Kelvin and Celsius. For Fahrenheit, the absolute scale is called Rankine.

YES!! Einstein was the most famous and one of the smartest people that existed He corrected Newtons theories of gravity Helped us understand how the universe worked Created the equation E=MC2 Made theory of relitivity

The Bohr model of the atom is a simple model that describes the structure of an atom. It suggests that electrons orbit the nucleus in specific energy levels or shells. Each shell can hold a specific number of electrons, and electrons can jump between shells by absorbing or emitting energy. The Bohr model helped explain the line spectra of different elements and laid the groundwork for understanding how electrons behave in atoms.

Cyclosporine was discovered by a team of researchers led by Hartmann F. Stähelin and Jean-François Borel in Switzerland in 1970. They were studying soil samples for potential antibiotic properties and identified cyclosporine as a compound produced by the fungus Tolypocladium inflatum.

A scientist that studies fungi is called a mycologist.

Mathieu Orfila is known as the father of forensic toxicology. He made significant contributions to the field by developing methods to detect poisons in the human body through chemical analysis in the early 19th century. Orfila's work helped establish toxicology as a legitimate science used in criminal investigations and legal proceedings.

The choices are

-they were harmed by radiation therapy

-they died off because the cancerous cells deprived them of nutrients

-they died off due to natural causes

- they thrived with the cancerous cells

Isotopes help scientists by providing unique markers for tracing biological and chemical processes, dating artifacts, and studying nutrient cycling in ecosystems. They also help in understanding geologic processes, tracking pollutants, and in medical diagnostics and treatments.

Age at death estimates from the skull are based on the extent to which the various bones of the cranium have fused together. Unfortunately its been shown that the rate at which this fusion takes place is fairly variable, meaning the method is not that accurate.

Where teeth remain these provide a far more accurate indicator.

In practice the most accurate calculations come from taking age at death estimates based on as much of the skeleton as is available, and so studies of just the skull, especially without teeth can only provide a rough estimate at best.

Albert Einstein attained a degree in physics at ETH Zürich -- the Swiss Federal Institute of Technology (FIT) in Zurich -- in 1900. He received a doctorate from the University of Zürich in 1905.

Scientists have updated their theories about Venus because of new data collected from recent spacecraft missions. These new findings have revealed a better understanding of Venus's atmosphere, surface, and geological features, prompting a revision of previous assumptions.

Standardized units of measure are important to engineering because if everyone was using different measurement systems, dimensions of different objects and inventions wouldn't be converted correctly from the inventor to other inventors or manufacturers.

(Had to be fixed because who ever wrote the originally answer was a a$$ hole)

The traditional view that one cannot prove the null hypothesis by a statistical analysis is a consequence of Ronald Fisher's structuring of the problem of probabilistic inference. Fisher argued that if one wanted to determine whether an experimental manipulation (e.g., a drug treatment or a treatment of some crop land) had an effect (on e.g., recovery rate or crop yield), one should compute the probability that one would obtain an effect (that is, a difference in the means of two samples, a control sample, and an experimental sample) as big or bigger than the one in fact obtained if both samples were in fact drawn from the same distribution (that is, if there were in fact no effect). In other words, how likely is it that one would see an effect that big or bigger by chance? If this probability is sufficiently low (say, less than one chance in 20), then one is justified in concluding that what one did had an effect (or that there was a difference in the average values in the populations that one drew the two samples from). Thus, if the difference in the means of the two samples was sufficiently greater than expected by chance, then one was justified in concluding that something more than chance was at work. This way of framing the problem has come to be called Null Hypothesis Significance Testing (NHST, for short). In this formulation, you cannot prove the null hypothesis, because failing to reject it is not the same as accepting it--anymore than a verdict of "not proven" is the same as a verdict of "not guilty." Thus, if you think this is the right way to formulate the problem of probabilistic inference, then you cannot prove the null.

But this way of formulating the problem flies in the face of common sense. We all draw a strong and clear distinction between "not proven" and "not guilty." In this formulation, the only possible verdict as regards the null hypothesis is "not proven." This would perhaps be okay if null hypotheses were of no scientific or practical importance. But, in fact, they are of profound scientific and practical importance. The conservation laws, which are at the foundation of modern physics, are null hypotheses; they all assert that "under no circumstance does this change." And, for most people it matters whether a generic drug costing 1/10 the cost of a brand drug really has "the same effect" as the brand drug or just "has not been proven to have a different effect." If you frame it in the latter way, then many more people will opt to pay the additional cost than if you say that "the evidence shows that the effects of the generic drug and the brand drug do not differ" (a null hypothesis).

Moreover, and this is more technical, the traditional formulation violates basic mathematical/logical considerations. One of these is consistency: a rational treatment of the evidence for and against any hypothesis should have the property that as the number of observations "goes to infinity" (becomes arbitrarily large), then the probability of drawing the correct conclusion should go to 1. But, under the NHST formulation, when the null hypothesis is true, the probability of rejecting it remains .05 or .01 (whatever one regards at the crucial degree of improbability) no matter how many observations there are. Another curious aspect of the traditional formulation is that it licenses concluding the one's own (non-null) hypothesis is correct because the null hypothesis appears to fail, even though one's own hypothesis is never tested against the data, whereas the null hypothesis is. This is a little bit like concluding that one could oneself climb a formidable mountain just because someone else has failed to climb it. Fairness would seem to require that one's own hypothesis, whatever it may be, should undergo the same test that the null hypothesis has undergone. At a somewhat simpler level, How can a statistical method for drawing conclusions be valid if it prohibits ever drawing a conclusion in favor of some hypotheses (null hypotheses) that are of fundamental scientific and practical importance?

There is an alternative to the NHST formulation of the problem of probabilistic inference that dates back to the work of the Reverend Thomas Bayes in the 18th century. In the Bayesian formulation, both the null hypothesis and one or more alternatives to it are tested against the data. In this formulation, each of the hypotheses places a bet on where the data from the experimental treatment (or the other condition of observation) will fall. The hypothesis that does the best job of anticipating where the data in fact fall obtains the greatest odds of being correct (or, at least, more valid than the alternatives to it that have been proposed). In this formulation, it is perfectly possible for the null hypothesis to be the odds on favorite. In other words, in this conception of how to do probabilistic inference, it is possible to prove the null in the sense that the null may have arbitrarily greater odds as against any of the proposed alternative to it. Thus, in this formulation, the null is no different than any other hypothesis. This approach to the problem of probabilistic inference has gained considerable currency in recent years. According to its advocates, it is the only "normative" (mathematically correct) formulation, because, among other things, it does not prohibit any hypothesis from being accepted, and because it is consistent: as the data (number of observations) become arbitrarily large, the odds that the true hypothesis will be accepted increase toward infinity, regardless of whether the true hypothesis is the null hypothesis or an alternative to it. In short, the Bayesian formulation places the null hypothesis on the same footing as any other hypothesis, so it is just as susceptible of proof as any other hypothesis.

The story goes.... In the search for the relationship between the known elements, Dmetri Mendeleev's devised a card game made of elements; sort of like 'elemental solitaire'. In this game, each card had one element written on it with its atomic weight. He spent 2 sleepless days attempting to find a relationship by grouping the cards together on the table. On the third day there was a snow storm and Mendeleev's decided to stay home. Although restless he eventually fell asleep in which he dreamed a scientific breakthrough. In this dream he saw the elements arranged in a table and they were grouped together by various properties. When he awoke, he attempted to group the known elements in this manner and noticed that in order for this arrangement to work, he needed to leave spaces or gaps for other elements. Therefore, he also discovered that there were many other elements needed to be discovered.

particles, which led to the development of the concept of wave-particle duality in quantum physics.

Nikola Tesla made numerous discoveries and inventions in the field of electrical engineering and physics. Some of his most notable discoveries include the rotating magnetic field, the Tesla coil, and contributions to the development of alternating current (AC) electrical systems. Tesla also did research on wireless communication and wireless energy transmission.