DEFINITION: Quantitative methods are research techniques that are used to gather quantitative data — information dealing with numbers and anything that is measurable e.g. Statistics, tables and graphs, are often used to present the results of these methods. Quantitative research methods were originally developed in the natural sciences to study natural phenomena. However examples of quantitative methods now well accepted in the social sciences and education. Differences between parametric and non parametric methods Parametric method Non parametric Assumes data comes from a type of probability distributions ( they are not distribution-free) Distribution free methods which do not rely on assumptions that the data are drawn from a …show more content…
To answer this question, the decision maker or statistician must reorganize that any such test procedure or decision rule can make either a type I error or a type II error. in this case, a Type I error will arise if the firm rejects a shipment in which the mean length of life of the bulbs is 2000 hours a Type II error will arise if the firm does not reject a shipment where the mean length of life of the bulbs is less than 2000 hours. - The possibility in this case , much as the possibility of type I error is designated α (alpha) and the possibility of a Type II error is designated as β (beta) - Possible outcomes of the decision problem concerning the shipment of the light bulbs. Table: Possible outcomes of the decision problem concerning the shipment of light Bulbs Alternative courses of action μ=2000 State of nature μ < 2000 Do not reject shipment Reject shipment Correct decision Type I error Type II error correct decision If μ >2000, the correct decision is to not reject the shipment 2 Specify the significance level of the test The value assigned to α is called the significance level of the test. Typically, the significance level is set at 0.05 or 0.01, which means that the probability of type I error is 0.05 or 0.01 The decision maker can set the significance level at any amount that he chooses. However, in setting this level it generally wise to keep in mind the relative costs of
15 In testing the hypotheses: H0 β1 ’ 0: vs. H1: β 1 ≠ 0 , the following statistics are available: n = 10, b0 = 1.8, b1 = 2.45, and Sb1= 1.20. The value of the test statistic is:
Step 2: During this second step, the characteristics of the comparison distribution is determined. In instances that the null theory is correct, the comparison distribution is compared to the score depending on the sample’s outcomes.
There are multiple values being considered in this study. This has the potential to cause a type 1 error and Bonferroni procedures should be used to reduce the risk of a type 1 error from happening.
that were not correct and correct them, giving the data from your experiment that supports the corrections.
The null hypothesis should be rejected that there is no difference between treatment methods because the calculation show that the treatments are statistically different.
A type I error is where "the null hypothesis is rejected when it should not be rejected" and a type
Answer: C2. In hypothesis testing, Type II error isA) equal to 1 - probability of committing Type I error.
Quantitative research is done to find the accurate facts by evaluating the problems like opinion, behaviour by using numerical data. This research is based on theories, hypothesis, collecting, analysing the data to make the research accurate.
From the calculation (See Appendix I), we get the 3-sigma control limits for the process, i.e. UCL=0.091, LCL=0.014. These control limits indicate that if the error proportion is within the range of [0.014, 0.091], the process is under control; if not, the process is out of control.
Quantitative research methods are objective as it uses measurements and analysis of statistical data to answer the study question. The researchers’ opinions do not affect the outcome of the study, ensuring that the study is unbiased. Another advantage is quantitative research uses numbers and statistics which is understood universally (Houser,2008).
4) Discuss the implications of changing the level of significance to a larger value. What mistakes or error could increase if the level of significance in
As a group we encountered several possible errors throughout the experiment. Blunder error is a common error than can occur.
Quantitative research involves collecting data, which can be expressed numerically. The design is well structured with pre-determined outcomes. It frequently involves testing a hypothesis, which then can be analysed from the data deductively using statistical methods. Using numerical data is easier to analysis mathematical, so larger sample sizes can be utilised compared to qualitative research, therefore giving a better representative of the population; along with simplifying the process of making a generalisation. Another advantage is that studies can easily compare to similar findings (Kruger, 2003). The disadvantages are the results are limited and might not provide a proper understanding of the topic. Also, statistics and leading questions can be used to give a false representation of the data when summarising.
A Type I error is the worst type of error a researcher can make while conducting an experiment. A Type I error is the rejection of the null hypothesis when the null hypothesis is actually true. Therefore, a Type I error is a false positive and it indicates that results are significant when they are not. For example, if a researcher states that the amount of antidepressants taken by individuals will decrease the number of suicides, the outcome could be potentially harmful. Individuals would become reliant on the medication and have expectations of the antidepressant lowering their likelihood of committing suicide. Unfortunately, this Type I error could potentially increase suicide. Setting a low alpha level will allow the researcher to avoid
Quantitative research methods are utilized to study the natural phenomena. So, it includes survey methods, formal methods, and numerical methods.