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Read Part G, Topics 47-61 and Part H, Topics 62-67 in our textbook.

Post a reading report on this discussion board forum answering the following questions:

1. What are the differences between descriptive and inferential statistics?

Descriptive stats summarize data so the data can be comprehended. The researchers prepare a frequency distribution which shows the frequencies as descriptive statistics. Percentages, and averages are also descriptive statistics. Therefore, the descriptive statistics describe sets of data collected through observation. Then the statistics are organized in tables, pie charts, graphs etc. Researchers must be sure the kind of descriptive statistics matches the kind of data that has been collected. Influential statistics is when, due to the size of the*…show more content…*

What is the null hypothesis? Why is the null hypothesis important?

A null hypothesis is when samples are taken in inferential statistics but those samples are unrepresentative because of random sampling errors. This can happen in three ways: 1. The observed difference was created by sampling errors, keeping in mind there is no bias because the survey is done randomly. 2. Null hypothesis also occurs when there is no true difference between the two groups. This meaning that the true difference is the difference a researcher would find if there were no sampling errors. 3. The true difference between the two groups is zero.

The null hypothesis is important to determine if there is a difference between the groups being tested or not. If the null hypothesis was not present, the number of possibilities would make it impossible to test. Also at the beginning of the research the null hypothesis can make the research more objectively based and play a key role in the statistical analysis as results become available. Ultimately, then, with the degree of the null hypothesis and its probability (p) it can be determined rather it is rejected in lieu of alternative hypothesis (Patten,

Post a reading report on this discussion board forum answering the following questions:

1. What are the differences between descriptive and inferential statistics?

Descriptive stats summarize data so the data can be comprehended. The researchers prepare a frequency distribution which shows the frequencies as descriptive statistics. Percentages, and averages are also descriptive statistics. Therefore, the descriptive statistics describe sets of data collected through observation. Then the statistics are organized in tables, pie charts, graphs etc. Researchers must be sure the kind of descriptive statistics matches the kind of data that has been collected. Influential statistics is when, due to the size of the

What is the null hypothesis? Why is the null hypothesis important?

A null hypothesis is when samples are taken in inferential statistics but those samples are unrepresentative because of random sampling errors. This can happen in three ways: 1. The observed difference was created by sampling errors, keeping in mind there is no bias because the survey is done randomly. 2. Null hypothesis also occurs when there is no true difference between the two groups. This meaning that the true difference is the difference a researcher would find if there were no sampling errors. 3. The true difference between the two groups is zero.

The null hypothesis is important to determine if there is a difference between the groups being tested or not. If the null hypothesis was not present, the number of possibilities would make it impossible to test. Also at the beginning of the research the null hypothesis can make the research more objectively based and play a key role in the statistical analysis as results become available. Ultimately, then, with the degree of the null hypothesis and its probability (p) it can be determined rather it is rejected in lieu of alternative hypothesis (Patten,

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## Cross Sectional Study

980 Words | 4 PagesThis was a descriptive study. 3.3. Sample techniques Probability sampling is also known as ‘... ... middle of paper ... ...well a research design (and the research method and the measures or question used) delivers accurate, clear and unambiguous evidence with which to answer the research problem. Validity is an indicator of whether the research measures what it claims to measure. In this study, reliability will not be used, because the sample size is based on a small sample selected.

## Qualitative Research Versus Quantitative Research

1612 Words | 7 PagesAnd if a group of researchers all researched the same topic would they all get different results? If so which should we believe. Researchers often combine quantitative and qualitative data in their research to get a fair and accurate result even thought quantitative is often more accurate than qualitative. The major difference between qualitative and quantitative research is the underlying statement about the role of the researcher. In quantitative research, the researcher is ideally an objective observer that neither participates in or influences what is being studied.

## Essay On Inferential Statistics

1942 Words | 8 PagesSignificance Testing Significance testing is directly related to probability. Probabilities that reject the null hypothesis generally start at 0.05 and can approach 0 depending on the value that the researchers choose. The significance level (α) is the maximum probability value that rejects the null hypothesis. Statistical significance is the term used when the null hypothesis has been rejected. It is important to note that the use of the word “significant” here does not correspond to the independent variable having a significant effect on the dependent variable.

## Experimental And Correlational Research

1064 Words | 5 PagesThey serve as base line to which to compare the experimental group too in order to identify an effect. The experimental group are under the exact same conditions except for an aspect that is changed in order to learn the result of this change. Cause-and-effect is the desired outcome from the experimental group in comparison to the control group. Randomisation is a key factor in the unbiased allocation of sub... ... middle of paper ... ...e research, there must be at least two measures, or it will be impossible to calculate a correlation. A correlation may be statistically significant but be weak or low which means it is not associated and has no practical significance.

## Descriptive Statistics Essay

2017 Words | 9 PagesType 2 in simplest terms, beta, is the same as false negatives. Meaning you may think that your experiment had no effect on the variable, but in reality it did. Alpha is considered a more desirable error than beta because at least with alpha, the attempt will be made. Sometimes in

## How Hard Should the Test Instances Be in Instance-Specific Macro Learning?

932 Words | 4 PagesMy argument is: putting in mind that we want to measure the significance in the difference of performance between the models/macro sets, and given that the process switching time of current operating systems is non zero, we should make such an assumption. This is because there will be a small ove... ... middle of paper ... ...h instances, and it was hard to avoid generating such instances for the test. It is not possible to completely control the output of a random problem generator, and the mprime problems were either relatively easy or extremely hard. So, the only way I found to make things more fair in the comparison was to apply the upper bound on the perfect model as discussed above. This method was very effective in showing that the perfect model is superior compared to the other macros/model.

## No Aid, No Violation: Answers to Questions

2618 Words | 11 PagesKim and Kolen (2010) pointed out that the unsmoothed method is most appropriate in the study in order to avoid to the influence of the smoothed equipercentile equating method (used to remove irregularities) on population invariance. For example, smoothing could produce lower standard errors than the USEE Kolen and Brennan (2004). As a result, unsmoothing equipercentile equating method was used. 3. The equally-weighted root expected square difference (ewREMSD) was used in the study to give equal weight to all score points and to examine the impact of the subgroups on the test takers success or failure designations.

## Entity Realism

2092 Words | 9 PagesPeople are driven towards realism because of the success of science.... ... middle of paper ... ...or a theory to be true there cannot even be the smallest bit of doubt, in the smallest bit of information which is part of the theory. The problem with theories is they attempt to claim too much. There is too much room for error in theories for them to be considered true. I agree with Ian Hacking who is an Entity Realist. Entity realists believe in things, but not theories.

## Statistics: Statistics And Statistics

675 Words | 3 PagesStatistics contains the development of procedures and tests that are used to describe the variability characteristic in data, the odds of certain outcomes, and the fault and doubt related with those outcomes. Some statistics are influenced, some are based on beliefs, and some are false. A frequent misunderstanding is that statistics gives a degree of proof that something is accurate. As an alternative, statistics provide a measure of the probability of observing a certain outcome. It is easy to mistreat statistics analysis even to the point of error because statistics do not familiarize us with organized or systematic error which can be carried into the data deliberately or unintentionally.

## Analysis Of Cronbach's Alpha And Spearman Brown Prophecy

1396 Words | 6 PagesIt randomly splits the test items in to two equal halves. The reliability is then measured for the two halves and compared. If the test is reliable, participants who scored low on one half should score low on the other half too. The value for this test was 0.708 which is just higher than 0.7, meaning it is acceptable. Because the split halves method only measures reliability for one of the halves, it therefore underestimates the whole test’s reliability (Terre Blanche et al., 2006).

### Cross Sectional Study

980 Words | 4 Pages### Qualitative Research Versus Quantitative Research

1612 Words | 7 Pages### Essay On Inferential Statistics

1942 Words | 8 Pages### Experimental And Correlational Research

1064 Words | 5 Pages### Descriptive Statistics Essay

2017 Words | 9 Pages### How Hard Should the Test Instances Be in Instance-Specific Macro Learning?

932 Words | 4 Pages### No Aid, No Violation: Answers to Questions

2618 Words | 11 Pages### Entity Realism

2092 Words | 9 Pages### Statistics: Statistics And Statistics

675 Words | 3 Pages### Analysis Of Cronbach's Alpha And Spearman Brown Prophecy

1396 Words | 6 Pages

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