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On several occasions, the health professional is faced with accepting or rejecting a proposition about some parameter of a population. In general, these propositions are called hypotheses, which are one of the most useful aspects of statistical inference and refer to the parameters of the populations for which the proposition is made. A very simplified scheme that summarizes the process would be the following:
1. Initial assertion.
2. Collect sample evidence linked to claims.
3. Based on sample evidence, reject or accept the initial claim
According to our Hypothesis Testing Experts, a statistical hypothesis is an assumption made regarding the distribution function of a random variable.
It is important to emphasize that hypotheses are always statements about the population or distribution understudy, not statements about the sample.
Scientists are interested in two kinds of hypothesis: the first one is statistical and next is research. The first is an estimation that motivated the investigation and the second is established or identified in such a way that they can be contrasted using adequate statistical tests. A statistical hypothesis is a proposition or assumption about the parameters of one or more populations.
The research hypothesis is formulated as a result of the occurrence of events that suggest that they influence some variable of interest that has been studied either in the short or even in the long term. For example, a nurse can have noticed that particular patients responded more quickly to a certain type of health education course.
A doctor will reminiscence numerous cases in which particular combinations of healing measures were more active than any one of them alone. Investigations are often carried out thanks to the aspiration of such professionals to determine whether or not their notions, assumptions or uncertainties can be sustained when imperilled to the rigours of technical investigation. Research hypothesis leads directly to statistical hypotheses.
A statistical hypothesis is intended to help the health professional or researcher decide a population based on a sample drawn from it. And the type of inference that is applied is hypothesis tests, which determine whether or not such propositions are compatible with the available data. But what is a hypothesis test? Since it is a procedure based on the evidence of the sample, it is the probability to determine if the hypothesis that is contrasted is a reasonable statement.
It is a process in five steps that follow a logical sequence of actions and decisions.
Stage (1) State the hypothesis- Hypothesis testing formulates two statistical hypotheses that must be explicitly announced: null and alternative hypotheses. The first is designated by the symbol H0.
Stage (2) Establish a level of significance- All the values that are possible to undertake are points on the horizontal axis of the distribution graph for this measurement and are divided into two groups: one of them establishes what is called rejection region and the other, form the non-rejection region.
Stage (3) Select the test value to apply- It is necessary to understand the quality of the data that forms the foundation of the testing measures, as this determines the particular test to be used.
Stage (4) Establish a decision rule- This indicates that H0 should be rejected if the value of the test statistic calculated from the sample is one of the values of the rejection region and that it should not be rejected if the calculated value is one of the non-rejected region values.
Stage (5) Make a decision or conclusion-This is nothing more than deciding if H 0 is rejected, then it is concluded that H 1 is true. If H 0 is not excluded, we determine that H 0 can be true. It is important to clarify that when H 0 is not rejected, it cannot be said that it is accepted either.
Stage (6) Decision Making- The perceived value of the sample statistic is linked to the critical value of the test statistic. Then the null hypothesis is to make sure to be true or false If this is false, the unconventional is accepted; in turn, this decision will have an impact on other decisions of operational managers, like whether or not to continue a performance standard or which of two marketing strategies to use.
|Possible Results||Possible Situations|
|The null hypothesis is true.||The null hypothesis is untrue.|
|Accept the Null Hypothesis||Accepted||Type 2 error|
|Reject the Null Hypothesis||Type 1 error||Rejected|
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