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Can you please answer each Discussion question approx. 1/2page each approx. 137.5 words Discussion 1 Part 1: Describe at least 4 key differences between men and women described by Helen Fisher. AND Part 2: What are some differences between male and female intimacy? Readings & Activities Fisher Chapter 10: Men and Women Are Like Two Feet: They Need Each Other to Get Ahead Gender Differences in Mind Associated Media: The Nature of Love link＝ https://www.youtube.com/watch?v＝UybJzG_dts4 Gender Differences in the Brain by Helen Fisher, Ph.D. (important video!) https://www.youtube.com/watch?v＝qSGd6Ojuw0Q Discussion 2 Read the material in the links to the left and check out the great videos below. 1. Are there any differences between gay and straight love? Why or why not? What does the evidence suggest regarding the similarities and differences between sexual majority and minority love? AND 2. Given what you have read, why is it so common for many heterosexuals to discuss and understand homosexuality only as same sex behavior, rather than as a complex identity no different than a heterosexual identity, with the drive to love and be loved at its core? NOTE: If you self identify as gay/lesbian/bi, what has been your experience with love? Share only what you are comfortable sharing. Readings & Activities [gaystraight-relationships-different/ link＝ https://glyswny.wordpress.com/lous-page/gaystraight-relationships-different/ ] [ gay-relationships-can-be-more-stable-straight-ones link＝ https://www.psychologytoday.com/us/blog/disturbed/201311/gay-relationships-can-be-more-stable-straight-ones ] [ www.ncbi.nlm.nih.gov/pmc/articles/ link＝ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3121169/ ] Associated Media: iO Tillett Wright: Fifty shades of gay link ＝ https://www.youtube.com/watch?v＝VAJ-5J21Rd0 Homosexual Love link ＝ https://www.youtube.com/watch?v＝v1kcxSPLOEs Discussion 3 What is the difference between ″normal″ mood variation and clinically significant mood variations? Provide several examples. Readings Chapter 7 Discussion 4 Podcast Relationship Matters Podcast Number 55 “Sexualized, objectified, but not satisfied”: What are three key takeaways from this podcast? What is your experience/reaction?

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Respond to the following in a minimum of 175 words:

Read the following scenario and explain what power issues may arise. What factors influence statistical power?

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A researcher is exploring differences between men and women on ‘number of different recreational drugs used.’ The researcher collects data on a sample of 50 men and 50 women between the ages of 18-25. Each participant is asked ‘how many different recreational drugs have you tried in your life?’ The IV is gender (male/female) and the DV is ‘number of reported drugs.’

**Part2**-PLEASE SEE ATTACHMENT

**PART3**-PLEASE SEE ATTACHMENT…THIS IS A GROUP ASSIGNMENT I ONLY HAVE TO COMPLETE A PART OF THE TABLE. I WILL POST MY PART ON TUESDAY

REFERENCE

LEARNING OBJECTIVES

- Explain how researchers use inferential statistics to evaluate sample data.
- Distinguish between the null hypothesis and the research hypothesis.
- Discuss probability in statistical inference, including the meaning of statistical significance.
- Describe the
*t*test and explain the difference between one-tailed and two-tailed tests. - Describe the
*F*test, including systematic variance and error variance. - Describe what a confidence interval tells you about your data.
- Distinguish between Type I and Type II errors.
- Discuss the factors that influence the probability of a Type II error.
- Discuss the reasons a researcher may obtain nonsignificant results.
- Define
*power*of a statistical test. - Describe the criteria for selecting an appropriate statistical test.

Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.

Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?

In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. **Inferential statistics** are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the *sample means* shown in Table 12.1 reflects a true difference in the *population means*.

Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred the Democratic candidate would be between 60% and 54% and the percentage preferring the Republican would be between 46% and 40%. In this case, the researcher could predict with a great deal of certainty that the Democratic candidate will win because there is no overlap in the projected population values. Note, however, that even when we are very (in this case, 95%) sure, we still have a 5% chance of being wrong.

Inferential statistics allow us to arrive at such conclusions on the basis of sample data. In our study with the model and no-model conditions, are we confident that the means are sufficiently different to infer that the difference would be obtained in an entire population?

Page 268

Much of the previous discussion of experimental design centered on the importance of ensuring that the groups are equivalent in every way except the independent variable manipulation. Equivalence of groups is achieved by experimentally controlling all other variables or by randomization. The assumption is that if the groups are equivalent, any differences in the dependent variable must be due to the effect of the independent variable.

This assumption is usually valid. However, it is also true that the difference between any two groups will almost never be zero. In other words, there will be some difference in the sample means, even when all of the principles of experimental design are rigorously followed. This happens because we are dealing with samples, rather than populations. Random or chance error will be responsible for some difference in the means, even if the independent variable had no effect on the dependent variable.

Therefore, the difference in the sample means does show any true difference in the population means (i.e., the effect of the independent variable) *plus* any random error. Inferential statistics allow researchers to make inferences about the true difference in the population on the basis of the sample data. Specifically, inferential statistics give the probability that the difference between means reflects random error rather than a real difference.

Statistical inference begins with a statement of the null hypothesis and a research (or alternative) hypothesis. The **null hypothesis** is simply that the population means are equal—the observed difference is due to random error. The **research hypothesis** is that the population means are, in fact, not equal. The null hypothesis states that the independent variable had no effect; the research hypothesis states that the independent variable did have an effect. In the aggression modeling experiment, the null and research hypotheses are:

*H*0 (null hypothesis): The population mean of the no-model group is equal to the population mean of the model group.

*H*1 (research hypothesis): The population mean of the no-model group is not equal to the population mean of the model group.

The logic of the null hypothesis is this: If we can determine that the null hypothesis is incorrect, then we accept the research hypothesis as correct. Acceptance of the research hypothesis means that the independent variable had an effect on the dependent variable.

The null hypothesis is used because it is a very precise statement—the population means are exactly equal. This permits us to know precisely the Page 269probability of obtaining our results if the null hypothesis is correct. Such precision is not possible with the research hypothesis, so we infer that the research hypothesis is correct only by rejecting the null hypothesis. We reject the null hypothesis when we find a very low probability that the obtained results could be due to random error. This is what is meant by **statistical significance:** A significant result is one that has a very low probability of occurring if the population means are equal. More simply, significance indicates that there is a low probability that the difference between the obtained sample means was due to random error. Significance, then, is a matter of probability.

**Probability** is the likelihood of the occurrence of some event or outcome. We all use probabilities frequently in everyday life. For example, if you say that there is a high probability that you will get an A in this course, you mean that this outcome is likely to occur. Your probability statement is based on specific information, such as your grades on examinations. The weather forecaster says there is a 10% chance of rain today; this means that the likelihood of rain is very low. A gambler gauges the probability that a particular horse will win a race on the basis of the past records of that horse.

Probability in statistical inference is used in much the same way. We want to specify the probability that an event (in this case, a difference between means in the sample) will occur if there is no difference in the population. The question is: What is the probability of obtaining this result if only random error is operating? If this probability is very low, we reject the possibility that only random or chance error is responsible for the obtained difference in means.

The use of probability in statistical inference can be understood intuitively from a simple example. Suppose that a friend claims to have ESP (extrasensory perception) ability. You decide to test your friend with a set of five cards commonly used in ESP research; a different symbol is presented on each card. In the ESP test, you look at each card and think about the symbol, and your friend tells you which symbol you are thinking about. In your actual experiment, you have 10 trials; each of the five cards is presented two times in a random order. Your task is to know whether your friend’s answers reflect random error (guessing) or whether they indicate that something more than random error is occurring. The null hypothesis in your study is that only random error is operating. In this case, the research hypothesis is that the number of correct answers shows more than random or chance guessing. (Note, however, that accepting the research hypothesis could mean that your friend has ESP ability, but it could also mean that the cards were marked, that you had somehow cued your friend when thinking about the symbols, and so on.)

Page 270You can easily determine the number of correct answers to expect if the null hypothesis is correct. Just by guessing, 1 out of 5 answers (20%) should be correct. On 10 trials, 2 correct answers are expected under the null hypothesis. If, in the actual experiment, more (or less) than 2 correct answers are obtained, would you conclude that the obtained data reflect random error or something more than merely random guessing?

Suppose that your friend gets 3 correct. Then you would probably conclude that only guessing is involved, because you would recognize that there is a high probability that there would be 3 correct answers *even though only 2 correct are expected under the null hypothesis.* You expect that exactly 2 answers in 10 trials would be correct in the long run, if you conducted this experiment with this subject over and over again. However, small deviations away from the expected 2 are highly likely in a sample of 10 trials.

Suppose, though, that your friend gets 7 correct. You might conclude that the results indicate more than random error in this one sample of 10 observations. This conclusion would be based on your intuitive judgment that an outcome of 70% correct when only 20% is expected is very unlikely. At this point, you would decide to reject the null hypothesis and state that the result is significant. A significant result is one that is very unlikely if the null hypothesis is correct.

A key question then becomes: How unlikely does a result have to be before we decide it is significant? A decision rule is determined prior to collecting the data. The probability required for significance is called the **alpha level.** The most common alpha level probability used is .05. The outcome of the study is considered significant when there is a .05 or less probability of obtaining the results; that is, there are only 5 chances out of 100 that the results were due to random error in one sample from the population. If it is very unlikely that random error is responsible for the obtained results, the null hypothesis is rejected.

You may have been able to judge intuitively that obtaining 7 correct on the 10 trials is very unlikely. Fortunately, we do not have to rely on intuition to determine the probabilities of different outcomes. Table 13.1 shows the probability of actually obtaining each of the possible outcomes in the ESP experiment with 10 trials and a null hypothesis expectation of 20% correct. An outcome of 2 correct answers has the highest probability of occurrence. Also, as intuition would suggest, an outcome of 3 correct is highly probable, but an outcome of 7 correct is highly unlikely.

The probabilities shown in Table 13.1 were derived from a probability distribution called the *binomial distribution;* all statistical significance decisions are based on probability distributions such as this one. Such distributions are called **sampling distributions.** The sampling distribution is based on the assumption that the null hypothesis is true; in the ESP example, the null hypothesis is that the person is only guessing and should therefore get 20% correct. Such a distribution assumes that if you were to conduct the study with the same number of observations over and over again, the most frequent finding would be 20%. However, because of the random error possible in each sample, there is a certain probability associated with other outcomes. Outcomes that are close to the expected null hypothesis value of 20% are very likely. However, outcomes farther from the expected result are less and less likely if the null hypothesis is correct. When your obtained results are highly unlikely if you are, in fact, sampling from the distribution specified by the null hypothesis, you conclude that the null hypothesis is incorrect. Instead of concluding that your sample results reflect a random deviation from the long-run expectation of 20%, you decide that the null hypothesis is incorrect. That is, you conclude that you have not sampled from the sampling distribution specified by the null hypothesis. Instead, in the case of the ESP example, you decide that your data are from a different sampling distribution in which, if you were to test the person repeatedly, most of the outcomes would be near your obtained result of 7 correct answers.

Page 271

TABLE 13.1 Exact probability of each possible outcome of the ESP experiment with 10 trials

All statistical tests rely on sampling distributions to determine the probability that the results are consistent with the null hypothesis. When the obtained data are very unlikely according to null hypothesis expectations (usually a .05 probability or less), the researcher decides to reject the null hypothesis and therefore to accept the research hypothesis.

The ESP example also illustrates the impact of sample size—the total number of observations—on determinations of statistical significance. Suppose you had tested your friend on 100 trials instead of 10 and had observed 30 correct answers. Just as you had expected 2 correct answers in 10 trials, you would now expect 20 of 100 answers to be correct. However, 30 out of 100 has a much Page 272lower likelihood of occurrence than 3 out of 10. This is because, with more observations sampled, you are more likely to obtain an accurate estimate of the true population value. Thus, as the size of your sample increases, you are more confident that your outcome is actually different from the null hypothesis expectation.

Different statistical tests allow us to use probability to decide whether to reject the null hypothesis. In this section, we will examine the *t* test and the *F* test. The **t**** test** is commonly used to examine whether two groups are significantly different from each other. In the hypothetical experiment on the effect of a model on aggression, a *t* test is appropriate because we are asking whether the mean of the no-model group differs from the mean of the model group. The *F* test is a more general statistical test that can be used to ask whether there is a difference among three or more groups or to evaluate the results of factorial designs (discussed in Chapter 10).

To use a statistical test, you must first specify the null hypothesis and the research hypothesis that you are evaluating. The null and research hypotheses for the modeling experiment were described previously. You must also specify the significance level that you will use to decide whether to reject the null hypothesis; this is the alpha level. As noted, researchers generally use a significance level of .05.

The sampling distribution of all possible values of *t* is shown in Figure 13.1. (This particular distribution is for the sample size we used in the hypothetical experiment on modeling and aggression; the sample size was 20 with 10 participants in each group.) This sampling distribution has a mean of 0 and a standard deviation of 1. It reflects all the possible outcomes we could expect if we compare the means of two groups *and* the null hypothesis is correct.

To use this distribution to evaluate our data, we need to calculate a value of *t* from the obtained data and evaluate the obtained *t* in terms of the sampling distribution of *t* that is based on the null hypothesis. If the obtained *t* has a low probability of occurrence (.05 or less), then the null hypothesis is rejected.

The *t* value is a ratio of two aspects of the data, the difference between the group means and the variability within groups. The ratio may be described as follows:

The group difference is simply the difference between your obtained means; under the null hypothesis, you expect this difference to be zero. The value of *t* increases as the difference between your obtained sample means increases. Note that the sampling distribution of *t* assumes that there is no difference in the population means; thus, the expected value of *t* under the null hypothesis is zero. The within-group variability is the amount of variability of scores about the mean. The denominator of the *t* formula is essentially an indicator of the amount of random error in your sample. Recall from Chapter 12 that *s*, the standard deviation, and *s*2, the variance, are indicators of how much scores deviate from the group mean.

Page 273

FIGURE 13.1

Sampling distributions of *t* values with 18 degrees of freedom

A concrete example of a calculation of a *t* test should help clarify these concepts. The formula for the *t* test for two groups with equal numbers of participants in each group is:

Page 274The numerator of the formula is simply the difference between the means of the two groups. In the denominator, we first divide the variance ( and ) of each group by the number of subjects in that group (*n*1 and *n*2) and add these together. We then find the square root of the result; this converts the number from a squared score (the variance) to a standard deviation. Finally, we calculate our obtained *t* value by dividing the mean difference by this standard deviation. When the formula is applied to the data in Table 12.1, we find:

Thus, the *t* value calculated from the data is 4.02. Is this a significant result? A computer program analyzing the results would immediately tell you the probability of obtaining a *t* value of this size with a total sample size of 20. Without such a program, there are Internet resources to find a table of “critical values” of *t* (http://www.statisticsmentor.com/category/statstables/) or to calculate the probability for you (http://vassarstats.net/tabs.html). Before going any farther, you should know that the obtained result is significant. Using a significance level of .05, the critical value from the sampling distribution of *t* is 2.101. Any *t* value greater than or equal to 2.101 has a .05 or less probability of occurring under the assumptions of the null hypothesis. Because our obtained value is larger than the critical value, we can reject the null hypothesis and conclude that the difference in means obtained in the sample reflects a true difference in the population.

You are probably wondering how the critical value was selected from the table. To use the table, you must first determine the **degrees of freedom** for the test (the term *degrees of freedom* is abbreviated as *df*). When comparing two means, you assume that the degrees of freedom are equal to *n*1 + *n*2 − 2, or the total number of participants in the groups minus the number of groups. In our experiment, the degrees of freedom would be 10 + 10 − 2 = 18. The degrees of freedom are the number of scores free to vary once the means are known. For example, if the mean of a group is 6.0 and there are five scores in the group, there are 4 degrees of freedom; once you have any four scores, the fifth score is known because the mean must remain 6.0.

In the table, you must choose a critical *t* for the situation in which your research hypothesis either (1) specified a direction of difference between the Page 275groups (e.g., group 1 will be greater than group 2) or (2) did not specify a predicted direction of difference (e.g., group 1 will differ from group 2). Somewhat different critical values of *t* are used in the two situations: The first situation is called a one-tailed test, and the second situation is called a two-tailed test.

The issue can be visualized by looking at the sampling distribution of *t* values for 18 degrees of freedom, as shown in Figure 13.1. As you can see, a value of 0.00 is expected most frequently. Values greater than or less than zero are less likely to occur. The first distribution shows the logic of a two-tailed test. We used the value of 2.101 for the critical value of *t* with a .05 significance level because a direction of difference was not predicted. This critical value is the point beyond which 2.5% of the positive values and 2.5% of the negative values of *t* lie (hence, a total probability of .05 combined from the two “tails” of the sampling distribution). The second distribution illustrates a one-tailed test. If a directional difference had been predicted, the critical value would have been 1.734. This is the value beyond which 5% of the values lie in only one “tail” of the distribution. Whether to specify a one-tailed or two-tailed test will depend on whether you originally designed your study to test a directional hypothesis.

The **analysis of variance,** or **F**** test,** is an extension of the *t* test. The analysis of variance is a more general statistical procedure than the *t* test. When a study has only one independent variable with *two* groups, *F* and *t* are virtually identical—the value of *F* equals *t*2 in this situation. However, analysis of variance is also used when there are more than two levels of an independent variable and when a factorial design with two or more independent variables has been used. Thus, the *F* test is appropriate for the simplest experimental design, as well as for the more complex designs discussed in Chapter 10. The *t* test was presented first because the formula allows us to demonstrate easily the relationship of the group difference and the within-group variability to the outcome of the statistical test. However, in practice, analysis of variance is the more common procedure. The calculations necessary to conduct an *F* test are provided in Appendix C.

The *F* statistic is a ratio of two types of variance: systematic variance and error variance (hence the term *analysis of variance*). **Systematic variance** is the deviation of the group means from the grand mean, or the mean score of all individuals in all groups. Systematic variance is small when the difference between group means is small and increases as the group mean differences increase. **Error variance** is the deviation of the individual scores in each group from their respective group means. Terms that you may see in research instead of systematic and error variance are *between-group variance* and *within-group variance.* Systematic variance is the variability of scores between groups, and error variance is the variability of scores within groups. The larger the *F* ratio is, the more likely it is that the results are significant.

Page 276

The concept of effect size was discussed in Chapter 12. After determining that there was a statistically significant effect of the independent variable, researchers will want to know the magnitude of the effect. Therefore, we want to calculate an estimate of effect size. For a *t* test, the calculation is

where *df* is the degrees of freedom. Thus, using the obtained value of *t*, 4.02, and 18 degrees of freedom, we find:

This value is a type of correlation coefficient that can range from 0.00 to 1.00; as mentioned in Chapter 12, .69 is considered a large effect size. For additional information on effect size calculation, see Rosenthal (1991). The same distinction between *r* and *r*2 that was made in Chapter 12 applies here as well.

Another effect size estimate used when comparing two means is called Cohen’s *d*. Cohen’s *d* expresses effect size in terms of standard deviation units. A *d* value of 1.0 tells you that the means are 1 standard deviation apart; a *d* of .2 indicates that the means are separated by .2 standard deviation.

You can calculate the value of Cohen’s *d* using the means (*M*) and standard dev

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QUESTION

FEB 07, 2019

One of the more common hybrid designs uses __gender__ as the non-experimental factor. In such a design, researchers cannot make causal claims (not even tentative causal claims) about the effects of gender. However, gender differences are a favorite topic in the popular press and sometimes the findings get discussed in ways that imply causality.

Conduct a search of popular media sources (newspapers, magazines, blogs, etc.) and locate articles focused on the differences between men and women.

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Select one story or report as the focus of your primary post. Reread the story or report and assess how the gender differences are discussed. Does the reporter or author accurately describe the findings as non-causal? Summarize the findings presented by the story or article and report how well you think the reporter or author did in describing gender as a non-experimental factor. Include the citation of your article in your post along with other reference citations to support your positions and conclusions.

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In the nursing course, you may have difficulties with literature reviews, annotated bibliographies, critical essays, and other assignments. Our nursing assignment writers will offer you professional nursing paper help at low prices.

Truth be told, sociology papers can be quite exhausting. Our academic writing service relieves you of fatigue, pressure, and stress. You can relax and have peace of mind as our academic writers handle your sociology assignment.

We take pride in having some of the best business writers in the industry. Our business writers have a lot of experience in the field. They are reliable, and you can be assured of a high-grade paper. They are able to handle business papers of any subject, length, deadline, and difficulty!

We boast of having some of the most experienced statistics experts in the industry. Our statistics experts have diverse skills, expertise, and knowledge to handle any kind of assignment. They have access to all kinds of software to get your assignment done.

Writing a law essay may prove to be an insurmountable obstacle, especially when you need to know the peculiarities of the legislative framework. Take advantage of our top-notch law specialists and get superb grades and 100% satisfaction.

We have highlighted some of the most popular subjects we handle above. Those are just a tip of the iceberg. We deal in all academic disciplines since our writers are as diverse. They have been drawn from across all disciplines, and orders are assigned to those writers believed to be the best in the field. In a nutshell, there is no task we cannot handle; all you need to do is place your order with us. As long as your instructions are clear, just trust we shall deliver irrespective of the discipline.

Our essay writers are graduates with bachelor's, masters, Ph.D., and doctorate degrees in various subjects. The minimum requirement to be an essay writer with our essay writing service is to have a college degree. All our academic writers have a minimum of two years of academic writing. We have a stringent recruitment process to ensure that we get only the most competent essay writers in the industry. We also ensure that the writers are handsomely compensated for their value. The majority of our writers are native English speakers. As such, the fluency of language and grammar is impeccable.

There is a very low likelihood that you won’t like the paper.

- When assigning your order, we match the paper’s discipline with the writer’s field/specialization. Since all our writers are graduates, we match the paper’s subject with the field the writer studied. For instance, if it’s a nursing paper, only a nursing graduate and writer will handle it. Furthermore, all our writers have academic writing experience and top-notch research skills.
- We have a quality assurance that reviews the paper before it gets to you. As such, we ensure that you get a paper that meets the required standard and will most definitely make the grade.

- The writer will revise the paper up to your pleasing. You have unlimited revisions. You simply need to highlight what specifically you don’t like about the paper, and the writer will make the amendments. The paper will be revised until you are satisfied. Revisions are free of charge
- We will have a different writer write the paper from scratch.
- Last resort, if the above does not work, we will refund your money.

Not at all. All papers are written from scratch. There is no way your tutor or instructor will realize that you did not write the paper yourself. In fact, we recommend using our assignment help services for consistent results.

We check all papers for plagiarism before we submit them. We use powerful plagiarism checking software such as SafeAssign, LopesWrite, and Turnitin. We also upload the plagiarism report so that you can review it. We understand that plagiarism is academic suicide. We would not take the risk of submitting plagiarized work and jeopardize your academic journey. Furthermore, we do not sell or use prewritten papers, and each paper is written from scratch.

You determine when you get the paper by setting the deadline when placing the order. All papers are delivered within the deadline. We are well aware that we operate in a time-sensitive industry. As such, we have laid out strategies to ensure that the client receives the paper on time and they never miss the deadline. We understand that papers that are submitted late have some points deducted. We do not want you to miss any points due to late submission. We work on beating deadlines by huge margins in order to ensure that you have ample time to review the paper before you submit it.

We have a privacy and confidentiality policy that guides our work. We NEVER share any customer information with third parties. **Noone will ever know that you used our assignment help services. ** It’s only between you and us. We are bound by our policies to protect the customer’s identity and information. All your information, such as your names, phone number, email, order information, and so on, are protected. We have robust security systems that ensure that your data is protected. Hacking our systems is close to impossible, and it has never happened.

You fill all the paper instructions in the order form. Make sure you include all the helpful materials so that our academic writers can deliver the perfect paper. It will also help to eliminate unnecessary revisions.

Proceed to pay for the paper so that it can be assigned to one of our expert academic writers. The paper subject is matched with the writer’s area of specialization.

You communicate with the writer and know about the progress of the paper. The client can ask the writer for drafts of the paper. The client can upload extra material and include additional instructions from the lecturer. Receive a paper.

The paper is sent to your email and uploaded to your personal account. You also get a plagiarism report attached to your paper.

** PLACE THIS ORDER OR A SIMILAR ORDER WITH US TODAY AND GET A PERFECT SCORE!!! **

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