Psychology homework help

A minimum of 100 words each and References Response (#1 – 6) KEEP RESPONSE WITH ANSWER EACH ANSWER NEED TO HAVE A SCHOLARY SOURCE with a Hyperlink

Make sure the Responses includes the Following: (a) an understanding of the weekly content as supported by a scholarly resource, (b) the provision of a probing question. (c) stay on topic

- I agree with you in your perception of the directional hypothesis (one-tailed tests) and non-directional hypothesis (two tailed tests). And adding to your insight I believe per the e-book that a directional hypothesis is a one-tailed hypothesis that predicts the way patterns or differences between groups will go concerning results that will be observed, such as which treatment group will perform best; and the directional hypothesis can utilize a two-group, between-subjects design while it predicts the nature of effects in the independent variable on the dependent variable (Myers & Hansen. 2012), while a non-directional is a two-tailed hypothesis that holds to statements that predict a difference between treatment groups without predicting the exact pattern of results. According to Simply Psychology (2013), the independent variable will have an effect on the dependent variable and is not specified. Therefore, a researcher would design a study with a non directional hypothesis to show how an independent variable will have an effect on the dependent variable, of which the direction of the effect is not specified but it will show the difference in how many numbers are correctly recalled in the study (Simply Psychology, 2013).
- A two-tailed hypothesis is used when there is a non-directional hypothesis. This is when the experimenter does not predict the exact pattern of results. The direction of the effect will be produced by manipulating the experiment. A one-tailed test is used when the experimenter has a hypothesis that is directional (Myers & Hansen, 2012). A person would design a study with a non-directional hypothesis when there will be a difference between the two treatment groups. Although a difference will be evident, there has not been an estimate on the results of the comparison. When a researcher states a non-directional hypothesis, he is prepared to accept differences that are extreme and can go in either direction (Myers & Hansen, 2012).
- Hypothesis testing is ran to determine whether a claim is true or not given a population parameter. A test that is conducted to show whether the mean of the sample is significantly greater than and significantly less than the mean of a population is considered a two-tailed test (Myers & Hansen. 2012).

Statistically significant means the difference between the results do not occur by random chance (Myers & Hansen. 2012). Two-tailed test also known as the non- directional hypothesis is defined as the standard test of significance to determine if there is a relationship between the variables in either direction.The two-tailed test predicts that there will be an effect but does not predict the directions of the results. Non- directional hypothesis is commonly used for exploratory research as in exploratory research the researcher may not be sure about the direction of they hypotheses. A two-tailed test or non-directional hypothesis is appropriate if there is any difference between the two groups you are comparing. For example if you are only interested in determining if Group A scored higher than Group B, and you are completely uninterested in possibility of Group A scoring lower than Group B, then you may want to use a one-tailed test. The main advantage of using a one-tailed test is that it has more statistical power than a two-tailed test at the same significance (alpha) level.

- The null hypothesis is a statement that the manipulation of an experiment does not have an important effect. When the performance of treatment groups are very parallel that the groups must have belonged to the same population, this is considered the statement (null hypothesis) (Myers & Hansen, 2012). At the beginning of an experiment, a random sample of subjects come from the same population. This is done to avoid generating treatment groups that are diverse from each other before the experiment begins. Since a null hypothesis should be able to be rejected by the researcher at the end of the experiment this makes it necessary in statistics. The research hypothesis is necessary for statistics because it allows the researcher to prove his alternative hypothesis about the data and prove that the null hypothesis is probably wrong (Myers & Hansen, 2012)
- Per the e-book, the null hypothesis (H0) is a statement that the performance of treatment groups is so similar that the groups must belong to the same population; which is a way of conveying that the experimental manipulation had no important effect (Myers & Hansen, 2012). An alternative hypothesis (H1) is a statement that the data came from different populations; the research hypothesis, which cannot be tested directly (Myers & Hansen, 2012). Hence, the null hypothesis is necessary in statistics because the researcher can hold to assuming that the null hypothesis is correct until evidence shows the assumption can be rejected as the researcher can then become confused because ideally samples are taken from the same population from the beginning; therefore, random samples of subjects from the same population are randomly assigned to avoid creating treatment groups that differ from one to the other before the experiment even begins (Myers & Hansen, 2012), as the independent variable is manipulated so that the treatment groups are exposed to different conditions, and then when the experiment is over the researcher is able to reject the null hypothesis through showing that the effect produced by the independent variable leads to real differences in the responses of the group (Myers & Hansen, 2012). Furthermore, the alternative hypothesis can never prove that research hypothesis is correct (Myers & Hansen, 2012), and there is no way to prove that the data came from different populations or that the independent variable caused the pattern of results. In this, the researcher must show that it is unlikely that the pattern occurred from chance variation within the population of which can only show that the null hypothesis is probably wrong (Myers & Hansen, 2012).
- Hope all is well. Great post. The hypothesis in the light of which the null hypothesis gets rejected is called the alternative hypothesis. It differs from the null hypothesis. The purpose of both the hypothesis’ is to provide an approximate description of the phenomenon and a relational statement to be directly tested. Both the hypothesis are required to be fragmented properly before the data collection and interpretation phase in the research. It gives the researcher direction to his research. The alternative hypothesis is generally denoted by H1. Both the hypothesis’ are important in formulation of a testing procedure.

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