Rakan anda merancang untuk menjalankan survei untuk kertas projeknya. Dia tidak
ID: 3254943 • Letter: R
Question
Rakan anda merancang untuk menjalankan survei untuk kertas projeknya. Dia tidak pernah belajar statistik. Bagaimana anda hendak menerangkan kepadanya tentang konsep Your friend plans to conduct a survey for her project paper. She has never studied statistics. How would you explain to her the concepts of i. Taburan Normal dan Taburan Piawai Normal. Normal and standard normal distribution. ii. Ujian parametric dan non-parametric. Parametric and Non-parametric test. iii. Hipotesis Nul dan alternatif. Null and alternative hypothesis. iv. Nilai-p dan ujian statistik. p-value and test statistics. v. Tahap a dan Ralat Jenis I. alpha - level and Type I error.Explanation / Answer
Solution:-
(i) Normal and Standard Normal Distribution
A normal distribution is an arrangement of a data set in which most values cluster in the middle of the range and the rest taper off symmetrically toward either extreme.
Height is one simple example of something that follows a normal distribution pattern: Most people are of average height, the numbers of people that are taller and shorter than average are fairly equal and a very small (and still roughly equivalent) number of people are either extremely tall or extremely short.
In a normal distribution, the mean, mode and median are all the same.
The standard normal distribution is a special case of the normal distribution . It is the distribution that occurs when a normal random variable has a mean of zero and a standard deviation of one.
That is, a "Normal Distribution" with:
• a mean (central value) of 0 and
• a standard deviation of 1.
(ii) Parametric and Non-Parametric tests.
In the literal meaning of the terms, a parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn, while a non-parametric test is one that makes no such assumptions. In this strict sense, "non-parametric" is essentially a null category, since virtually all statistical tests assume one thing or another about the properties of the source population(s).
(iii) Null and alternative hypothesis.
Hypothesis testing involves the careful construction of two statements: the null hypothesis and the alternative hypothesis.
A null hypothesis is a statement, in which there is no relationship between two variables.
It is what the researcher tries to disprove. It is denoted by H-zero, and is always given with equal to sign.
An alternative hypothesis is statement in which there is some statistical significance between two measured phenomenon.
It is what the researcher tries to prove. It is denoted by H-one or H-a. It can take, not equal, < , > sign, in mathematical representation.
(iv) P-value and test statistics.
The P value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H 0) of a study question is true – the definition of 'extreme' depends on how the hypothesis is being tested.
A test statistic is a standardized value that is calculated from sample data during a hypothesis test. You can use test statistics to determine whether to reject the null hypothesis. The test statistic compares your data with what is expected under the null hypothesis. The test statistic is used to calculate the p-value.
A test statistic measures the degree of agreement between a sample of data and the null hypothesis. Its observed value changes randomly from one random sample to a different sample. A test statistic contains information about the data that is relevant for deciding whether to reject the null hypothesis.
(v) Alpha - level and Type I error
The significance level, also denoted as alpha or , is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference.
The significance level is the probability of making the wrong decision when the null hypothesis is true. Alpha levels (sometimes just called “significance levels”) are used in hypothesis tests. Usually, these tests are run with an alpha level of .05 (5%), but other levels commonly used are .01 and .10.
An alpha level is the probability of a type I error, or you reject the null hypothesis when it is true.
When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is , which is the level of significance you set for your hypothesis test. An of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for . However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.
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