Chi-Square is a statistical test that is utilized to make comparisons of observed data with data that the researcher expects to find with respect to a specified hypothesis. The test is used to determine whether the deviations in the data observed from the expected data have occurred just by chance or is caused by other factors (Brooks, 2008). The Chi-Square is usually employed to test the null hypothesis. For instance, it can be used to test whether there is no significant difference between the expected and observed outcomes. The Chi-Square is used in two circumstances as below: i) When the researcher want to estimate how closely the observed distribution matches the proportions that is expected. This is called ‘goodness of fit’ test. ii) When the researcher wishes to estimate whether random variables used are independent. Assumptions of the Chi-Square Test: i) To use Chi-Square test for independence, the two variables that are used must be of categorical data i.e. the data ought to be measured at nominal or ordinal levels. Furthermore, the two variables used ought to be composed of at least two categorical and independent groups (Brooks, 2008). For instance, ethnicity could consist of two groups i.e. Hispanic, Caucasian, and American) and gender can consist of two groups of females and males. ii) When using Chi-Square, the data ought not to be correlated. Therefore, the test cannot be performed when the data employed in the research is correlated. iii) The data must also be quantitative and the observations that are made must be independent. This means that the Chi-Square cannot be used when the data that is used in the research is qualitative. iv) The Sample size should be sufficiently large. This means that the sample size... ... middle of paper ... ...hip between the two variables. A regression coefficient close to zero means there is a weak relationship between the two variables. On the other hand, a regression coefficient close to 1 shows a strong relationship between the two variables. I will use Chi-test to address the study hypothesis. This is because the test is normally used when the researcher wants to determine whether there are differences in the categorical variables. For instance, social features such as religion, political differences, ethnical differences, etc. Therefore, I will come up with two hypotheses. Afterward, I will choose the significance level, calculate the test value then compare this to the critical value. If the test value is less than the critical value, I will not reject null hypothesis. However, if the test value is larger than the critical value, I will reject the null hypothesis.
Within the target site of the experiment, researchers wanted to answer their hypothesis; hypothesis was that increased police
The question for our research will be very relevant to our statistical test, therefore, the question will be: Is there going to be any significant dissimilarity amongst quiz 3 in different sections of our data set? Then the null hypothesis question is: Are there (no) any difference in the Quiz 3 by the sections. Then our alternative hypothesis will be the quiz 3 by the section. Therefore, we will have an alpha level of 5%.
Inferential Statistics has two approaches for making inferences about parameters. The first approach is the parametric method. The parametric method either knows or assumes that the data comes from a known type of probability distribution. There are many well-known distributions that parametric methods can be used, such as the Normal distribution, Chi-Square distribution, and the Student T distribution. If the underlying distribution is known, then the data can be tested accordingly. However, most data does not have a known underlying distribution. In order to test the data parametrically, there must be certain assumptions made. Some assumptions are all populations must be normal or at least same distribution, and all populations must have the same error variance. If these assumptions are correct, the parametric test will yield more accurate and precise estimates of the parameters being tested. If these assumptions are incorrect, the test will have a very low statistical power. This will reduce the probability of rejecting the null hypothesis when the alternative hypothesis is true. So what happens with the data is definitely known not to fit any distribution? This is when nonparametric methods are used.
To make sure it is a fair test; the procedure is repeated a couple of
...ne’s level of interest. The independent variables are the three different groups that are being studied. The ratings given by the participants will represent the dependent variables. The alpha is set as 0.05. According to SPSS, the results show that this study has a significance level of 0.000, which is less than 0.05. Because of this difference, it is appropriate to accept the research hypothesis and to reject the null hypothesis.
Personality test are made for people to help them understand more about themselves and why they are the way they are. I took one for Developmental Psychology and based on the choices I made on the questions, it determined my personality. The two personality test I took helped me figure out how I am.
we cant just look at one variable if we want it to be a fair test
The Myers-Briggs personality test analyzes your personality after answering a series of questions. Resulting from that is four letters. These letters are from eight different personalities and create sixteen combinations. The first being extroversion or introversion. Secondly, either sensing or intuition. Then thinking or feeling. Finally you are perceiving or judging. My results were Extroversion (E), Intuition (N), Thinking (T), and Perceiving (P). For each of those the percentages were 60%, 70%, 80%, and 80%. The reason for this test was to discover my personality and figure out how it could be beneficial. For example, when deciding on a career path knowing what might interest you helps. Also, to prove if this test was accurate in finding out your personality and if others agree with the results. When analyzing these traits for my personality looking at my behavior was important in deciding if it made sense, which almost they all did. The main purpose of this was to discover my personality and how it benefits knowing that.
An example of a null hypothesis for the variables used in this data collection would be, “Does GPA predicts final exam scores? An alternative hypothesis would be that GPA scores do determine the exam scores.
The research hypothesis is to test what is most effective/safe when college students consume energy drinks and with alcohol. The hypothesis statement describes the expected relationship between the independent and dependent variables in this research. In this case the relationship are positive or negative as one increases or decreases relationship. The hypothesis is not clear, but it should or could be. (Creswell, J. W., 2013).
Null hypothesis (pg. 49) – a type of hypothesis in which there is no relationship between the measured variables, and offers no support to the original hypothesis. An example of a null hypothesis would be that there was no relationship between time played and the number of concussions sustained by players who had high playing times.
Here the experimenter seeks knowledge by forming and testing a hypothesis. Scientific Method consists of an experiment, systematic observation and measurement, and testing, formulation and modification of hypothesis, which aids in producing results and creating a conclusion. This method of research is used often, as to maintain the objectivity, focus, and consistency of the study, by following these steps: (1) Ask a question, (2) Research existing sources, (3) Formulate a hypothesis, (4) Design and conduct a study, (5) Draw hypothesis and (6) Report results. These steps help to ensure the reliability, accuracy, and validity of study, results, and
In order for an experiment to be considered a true experimental design, the design must fit specific criteria. The researcher must have a hypothesis for a cause and effect relationship between variables, the treatment group, the control group, random selection for the treatment group, and random assignment for the control group. In a simple experiment, the researcher forms two groups that are similar or equivalent, through probability, to each other in every way possible appropriate to the concept of experiment. The treatment group receives the procedure for the experiment and the control group does not. Therefore, the only difference between the groups will be that one group receives the treatment for the experiment and one group does note. After the experiment is conducted the researcher analysis the results in both groups.
Culture-fair tests are non-verbal group intelligence tests that do not rely on a subject’s cultural skills, values, or language. For example in The Goodenough-Harris Drawing Test, subjects are asked to draw a picture of a person. Analysts examine the pictures proportions, details, representations of the body parts, etc. Another example of a culture-fair test is the Progressive Matrices test where subjects are given a picture with a missing part than asked to complete the picture with a number of possible choices.
I mean lets say we made a tree diagram based on how many heads and tales we got. We would do this test over and over again till we could determine the likelihood that we had a higher or lower prospect of spinning a head or a tail: