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Is the average number of television watched per evening in Australia less than in Britain Null and Alternative Hypothesis required?
Wiki User
∙ 7y ago
Null hypothesis: the number of TVs watched per evening in Australia and the UK are the same.Alternative hypothesis: the number of TVs watched per evening in Australia are fewer than in the UK.
The hypotheses should actually make clear whether the comparison is between the length of time that the TV programme is viewed, or number of programmes watched, and also whether a family of four watching together counts as 1 unit or 4. Also, what do you do with a TV set which is on but is not being watched seriously - my kids do it all the time!
Wiki User
∙ 6y ago
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What if hypothesis is not supporter by the data has been collected and analyzed?
When a hypothesis is not supported by data, two possibilities exist. The hypothesis can either be rejected and a new one is formulated, or more data is required to retest the hypothesis.
Why are repeated trials required before accepting a hypothesis are true?
They're required because a group of scientist can put down false data. So this means that other groups have to be able to find the exact data that the other group of scientist did.
Why are repeated trials required before accepting a hypothesis as true?
They're required because a group of scientist can put down false data. So this means that other groups have to be able to find the exact data that the other group of scientist did.
What does the word experiment mean?
An experiment is an investigation or test that is required to either prove or disprove a hypothesis(an educated guess or a thought anwer to a question that is unproven). Simply put an experiment is a test that is carried out to find a solution to a problem.
What are step of sciencetific method exp?
Here, let me GOOGLE that for you. ;)Formulate a question: The question can refer to the explanation of a specific observation, as in "Why is the sky blue?", but can also be open-ended, as in "Does sound travel faster in air than in water?" or "How can I design a drug to cure this particular disease?" This stage also involves looking up and evaluating previous evidence from other scientists, as well as considering one's own experience. If the answer is already known, a different question that builds on the previous evidence can be posed. When applying the scientific method to scientific research, determining a good question can be very difficult and affects the final outcome of the investigation.[10]Hypothesis: An hypothesis is a conjecture, based on the knowledge obtained while formulating the question, that may explain the observed behavior of a part of our universe. The hypothesis might be very specific, e.g., Einstein's prediction of the orbit of Mercury, or it might be broad, e.g., unknown species of life will be discovered in the unexplored depths of the oceans. A statistical hypothesis is a conjecture about some population. For example, the population might be people with a particular disease. The conjecture might be that a new drug will cure the disease in some of those people. Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false, e.g., that the new drug does nothing and that any cures are due to chance effects. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome, e.g., that the drug does better than chance. A final point: a scientific hypothesis must be falsifiable, meaning that one can identify a possible outcome of an experiment that conflicts with predictions deduced from the hypothesis; otherwise, it cannot be meaningfully tested.Prediction: This step involves determining the logical consequences of the hypothesis. One or more predictions are then selected for further testing. The less likely that the prediction would be correct simply by coincidence, the stronger evidence it would be if the prediction were fulfilled; evidence is also stronger if the answer to the prediction is not already known, due to the effects of hindsight bias (see also postdiction). Ideally, the prediction must also distinguish the hypothesis from likely alternatives; if two hypotheses make the same prediction, observing the prediction to be correct is not evidence for either one over the other. (These statements about the relative strength of evidence can be mathematically derived using Bayes' Theorem.)Test: This is an investigation of whether the real world behaves as predicted by the hypothesis. Scientists (and other people) test hypotheses by conducting experiments. The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from an hypothesis. If they agree, confidence in the hypothesis increases; otherwise, it decreases. Agreement does not assure that the hypothesis is true; future experiments may reveal problems. Karl Popper advised scientists to try to falsify hypotheses, i.e., to search for and test those experiments that seem most doubtful. Large numbers of successful confirmations are not convincing if they arise from experiments that avoid risk.[11] Experiments should be designed to minimize possible errors, especially through the use of appropriate scientific controls. For example, tests of medical treatments are commonly run as double-blind tests. Test personnel, who might unwittingly reveal to test subjects which samples are the desired test drugs and which are placebos, are kept ignorant of which are which. Such hints can bias the responses of the test subjects. Failure of an experiment does not necessarily mean the hypothesis is false. Experiments always depend on several hypotheses, e.g., that the test equipment is working properly, and a failure may be a failure of one of the auxiliary hypotheses. (See the Duhem-Quine thesis.) Experiments can be conducted in a college lab, on a kitchen table, at CERN's Large Hadron Collider, at the bottom of an ocean, on Mars (using the working rover), and so on. Astronomers do experiments, searching for planets around distant stars. Finally, most individual experiments address highly specific topics for reasons of practicality. As a result, evidence about broader topics is usually accumulated gradually.Analysis:This involves determining what the results of the experiment show and deciding on the next actions to take. The predictions of the hypothesis are compared to those of the null hypothesis, to determine which is better able to explain the data. In cases where an experiment is repeated many times, a statistical analysis such as a chi-squared test may be required. If the evidence has falsified the hypothesis, a new hypothesis is required; if the experiment supports the hypothesis but the evidence is not strong enough for high confidence, other predictions from the hypothesis must be tested. Once a hypothesis is strongly supported by evidence, a new question can be asked to provide further insight on the same topic. Evidence from other scientists and one's own experience can be incorporated at any stage in the process. Many iterations may be required to gather sufficient evidence to answer a question with confidence, or to build up many answers to highly specific questions in order to answer a single broader question.
What is the difference between a test statistic and a critical value?
A test statistic is a value calculated from a set of observations. A critical value depends on a null hypothesis about the distribution of the variable and the degree of certainty required from the test. Given a null hypothesis it may be possible to calculate the distribution of the test statistic. Then, given an alternative hypothesis, it is may be possible to calculate the probability of the test statistic taking the observed (or more extreme) value under the null hypothesis and the alternative. Finally, you need the degree of certainty required from the test and this will determine the value such that if the test statistic is more extreme than the critical value, it is unlikely that the observations are consistent with the hypothesis so it must be rejected in favour of the alternative hypothesis. It may not always be possible to calculate the distribution function for the variable.
What causes a hypothesis to be rejected?
A hypothesis will be rejected if it fails the necessary testing required for it to become a scientific theory.
What is the duration of Some Assembly Required TV series?
The duration of Some Assembly Required - TV series - is 3600.0 seconds.
When was Some Assembly Required - TV series - created?
Some Assembly Required - TV series - was created on 2007-12-27.
When did Some Assembly Required - TV series - end?
Some Assembly Required - TV series - ended on 2008-08-28.
Tradeoffs in cost involve examining the development of alternative designs and the required industrial base capability.Tradeoffs in cost involve examining the development of alternative designs and th?
methods of production
What is the required television size for PlayStation 3?
There isn't a required size but the graphics are better on a flat screen in high definition.
Are credit cards required for a satellite television provider?
Credit cards are not required in order the have satellite television. Other payment methods are allowed. My family pays through debit cards, not credit.
If Relationship between statistical significance and rejectingaccepting an hypothesis?
I believe you asked for the relationship between "statistical significance" and hypothesis testing. In hypothesis testing, we state the null and alternative hypothesis, then in the traditional method, we use a test statistic and a significance level, alpha, to decide whether to accept or reject the null hypothesis in favor of the alternative. If our test statistic falls in the reject area (critical region) of the sampling distribution, then we reject the null hypothesis. If not, we accept it. There is the second method, the p-value method, which is similar in that an alpha value has to be selected. Now, the term "statistical significant result", as used in statistics, means a result (mean value, proportion or variance) from a random sample was not likely to be produced by chance. When we reject the null hypothesis in favor of the alternative, we indicate our data supports an alternative hypothesis, so our result is "statistically significant." Let me use an example. Generally workers arrive at work a few minutes more or less than required. Our null hypothesis will be an average lateness of 5 minutes, and our alternative hypothesis will be greater than 5 minutes. Our data shows an average lateness of 12 minutes, and our test statistic, taking into account the variance and sample size, and our chosen alpha level, concludes that we reject the null hypothesis, so the 12 minute average is a significantly significant result because it supported rejection of the hypothesis. The problem is that significant, in common usage, means important or meaningful, not trivial or spurious. The sample used to calculate late time may have been not randomly chosen, more people come to work late in bad weather. The sample is to make inferences on the a general population, but there is no static population in this case, as a company hires and fires employees. So, since our data is flawed, so can our conclusions. Used as a technical term in statistics, statistical significance has a much more rigorous and restricted meaning, which can lead to confusion. See: http://en.wikipedia.org/wiki/Statistical_significance
What if hypothesis is not supporter by the data has been collected and analyzed?
When a hypothesis is not supported by data, two possibilities exist. The hypothesis can either be rejected and a new one is formulated, or more data is required to retest the hypothesis.
How is tv related to science?
the invention of TV required the knowledge of light properties and radio wave properties.
Is a license required to export ready-made garments from Gujarat India to Australia?
No license required
