Examples of common cause hypothesis in the following topics:
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- Rejecting the null hypothesis does not necessarily prove the alternative hypothesis.
- The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis.
- Alternatively, if the testing procedure forces us to reject the null hypothesis ($H_0$), we can accept the alternative hypothesis ($H_1$) and we conclude that the research hypothesis is supported by the data.
- Rejection of the null hypothesis is a conclusion.
- We might accept the alternative hypothesis (and the research hypothesis).
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- State why the probability value is not the probability the null hypothesis is false
- Explain why a non-significant outcome does not mean the null hypothesis is probably true
- Misconceptions about significance testing are common.
- It is the probability of the data given the null hypothesis.
- It is not the probability that the null hypothesis is false.
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- The notion of statistical error is an integral part of hypothesis testing.
- What we actually call type I or type II error depends directly on the null hypothesis, and negation of the null hypothesis causes type I and type II errors to switch roles.
- A type I error occurs when the null hypothesis ($H_0$) is true but is rejected.
- A common example is a guilty prisoner freed from jail.
- A false positive (with null hypothesis of health) in medicine causes unnecessary worry or treatment, while a false negative gives the patient the dangerous illusion of good health and the patient might not get an available treatment.
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- A hypothesis is a potential answer to your research question; the research process helps you determine if your hypothesis is true.
- This is an example of a causal hypothesis.
- To test this hypothesis, he compared twenty different regional Italian governments.
- While there is no single way to develop a hypothesis, a useful hypothesis will use deductive reasoning to make predictions that can be experimentally assessed.
- In research, independent variables are the cause of the change.
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- Explain why the null hypothesis should not be accepted when the effect is not significant
- By one common convention, if the probability value is below 0.05, then the null hypothesis is rejected.
- Another convention, although slightly less common, is to reject the null hypothesis if the probability value is below 0.01.
- If the null hypothesis is false, then it is impossible to make a Type I error.
- A Type II error can only occur if the null hypothesis is false.
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- The null hypothesis was that the Lady had no such ability.
- Fisher asserted that no alternative hypothesis was (ever) required.
- The typical line of reasoning in a hypothesis test is as follows:
- Common values are 5% and 1%.
- The former process was advantageous in the past when only tables of test statistics at common probability thresholds were available.
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- This is the central idea of the Prion Hypothesis, which remains debated.
- All known mammalian prion diseases are caused by the so-called prion protein, PrP.
- Fungal prions do not appear to cause disease in their hosts.
- The virion hypothesis states that TSEs are caused by a replicable informational molecule (likely to be a nucleic acid) bound to PrP.
- Compare the protein-only hypothesis of prion diseases with the virion hypothesis, as well as the heterodimer model and the fibril model of prion replication
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- Develop a hypothesis (if there appears to be a cause for the outbreak).
- Common source – All victims acquire the infection from the same source (e.g. a contaminated water supply).
- Continuous source – Common source outbreak where the exposure occurs over multiple incubation periods.
- Point source – Common source outbreak where the exposure occurs in less than one incubation period.
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- Descriptive studies describe the nature of the relationship between the intended variables, without looking at cause or effect.
- Future testing may disprove the hypothesis.
- Hypothesis testing is a type of statistics that determines the probability of a hypothesis being true or false.
- If the hypothesis is false, create a new hypothesis or try again
- After making a hypothesis, the researcher will then design an experiment to test his or her hypothesis and evaluate the data gathered.
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- Simply put, it
says look/listen, infer a cause and test your inference.
- An hypothesis is a declarative sentence that sounds like a fact...but isn’t!
- Design an experiment to test the hypothesis: results must be measurable evidence for or against the hypothesis.
- Beyond these most common parts of the scientific method, most descriptions add two more precepts:
- These Laws are thought of as universal and are most common in math and physics.