零假设是指现象或种群之间没有影响或没有关系的命题。任何观测到的差异都是由于采样误差（随机机会）或实验误差。空假设很流行，因为它可以被测试并发现是错误的，这意味着观测数据之间存在关系。也许更容易把它看作一个无效的假说或研究人员试图推翻的假说。另一种假设，HA或H1，提出观测受非随机因素的影响。在一项实验中，替代假设表明实验变量或自变量对因变量有影响。同样众所周知的是：H0，无差异假设有两种陈述无效假设的方法。一个是陈述为陈述句，另一个是呈现为数学陈述。例如，研究人员怀疑运动与减肥有关，假设饮食没有改变。当一个人一周锻炼五次时，达到某种减肥效果的平均时间是6周。研究人员想测试如果每周锻炼次数减少到3次，减肥是否需要更长的时间。编写空假设的第一步是找到（备选）假设。总之，像这样的问题，你们在寻找你们期望的结果。在这种情况下，假设是“我预计减肥需要超过6周的时间”。在这种情况下，如果在超过6周内没有实现减肥，那么它必须在等于或小于6周的时间发生。另一种陈述无效假设的方法是不对实验结果进行假设。在这种情况下，无效假设只是治疗或改变对实验结果没有影响。对于这个例子，减少锻炼次数不会影响减肥的时间：“多动症与吃糖无关”是无效假设的一个例子。如果用统计学检验假说并且发现是错误的，那么多动症和糖摄取之间可能存在联系。显著性检验是用于建立对空假设的信任的最常见的统计检验。另一个零假设的例子是：“植物生长速率不受土壤中镉的存在影响。”研究者可以通过测量在缺乏镉的培养基中生长的植物的生长速度与在植物中生长的植物的比率来检验这个假说。含不同量的镉。验证零假设将为进一步研究土壤中不同浓度元素的影响奠定基础。你可能想知道为什么你要测试一个假设，只是发现它是假的。为什么不去测试另一个假设并发现它是真的？简短的答案是，这是科学方法的一部分。在科学中，“证明”某事不会发生。科学用数学来确定一个陈述是真是假的概率。事实证明，证明一个假设比证明一个假设要容易得多。此外，虽然空假设可以简单地陈述，但是备选假设很可能是不正确的。例如，如果您的零假设是植物生长不受日照时间的影响，那么您可以以几种不同的方式陈述备选假设。有些陈述可能是错误的。你可以说植物受到超过12小时的阳光的伤害，或者植物至少需要3小时的阳光，等等。这些替代假设有明确的例外，所以如果你测试了错误的植物，你可能会得出错误的结论。空假设是一个通用的陈述，可以用来发展一个替代的假设，这可能是正确的，也可能是不正确的。

英国谢菲尔德大学论文代写:零假设

Zero hypothesis refers to the proposition that there is no influence or relationship between phenomena or populations. Any observed difference is due to sampling error (random chance) or experimental error. Empty hypothesis is popular because it can be tested and found to be wrong, which means that there is a relationship between observations. It may be easier to see it as an invalid hypothesis or a hypothesis that researchers are trying to disprove. Another hypothesis, HA or H1, suggests that observations are influenced by non-random factors. In one experiment, the substitution hypothesis showed that experimental variables or independent variables had an effect on dependent variables. It is also well known that H0, the indifference hypothesis has two ways of stating invalid hypothesis. One is to present a statement as a declarative sentence, the other is to present it as a mathematical statement. For example, researchers suspect that exercise is associated with weight loss, assuming that diet remains unchanged. When a person exercises five times a week, the average time to achieve some weight loss effect is six weeks. Researchers wanted to test whether it would take longer to lose weight if the number of exercises per week was reduced to three. The first step in writing null hypotheses is to find (alternative) hypotheses. In short, you’re looking for the results you want with questions like this. In this case, suppose “I expect to lose weight for more than six weeks”. In this case, if weight loss is not achieved in more than six weeks, it must occur in a time equal to or less than six weeks. Another way to state the invalid hypothesis is to make no assumptions about the experimental results. In this case, the invalid hypothesis is that treatment or change has no effect on the experimental results. For this example, reducing the number of exercises does not affect the weight loss time: “ADHD is not related to sugar consumption” is an example of the null hypothesis. If the hypothesis is tested by statistics and found to be wrong, there may be a link between hyperactivity disorder and sugar intake. Significance test is the most common statistical test used to establish trust in null hypothesis. Another example of zero hypothesis is: “Plant growth rate is not affected by the presence of cadmium in soil.” Researchers can test this hypothesis by measuring the growth rate of plants growing in cadmium-deficient media versus the ratio of plants growing in plants. It contains different amounts of cadmium. Verifying the zero hypothesis will lay a foundation for further study of the effects of different concentrations of elements in soil. You may want to know why you test a hypothesis, only to find that it is false. Why not test another hypothesis and find it true? The short answer is that this is part of the scientific approach. In science, “prove” that something will not happen. Science uses mathematics to determine the probability that a statement is true or false. Facts have proved that it is much easier to prove a hypothesis than to prove a hypothesis. In addition, although null hypothesis can be simply stated, alternative hypothesis is likely to be incorrect. For example, if your zero hypothesis is that plant growth is not affected by sunshine time, you can state alternative hypotheses in several different ways. Some statements may be wrong. You can say that plants are damaged by more than 12 hours of sunlight, or that plants need at least 3 hours of sunlight, and so on. There are clear exceptions to these alternative assumptions, so if you test the wrong plant, you may come to the wrong conclusion. Empty hypothesis is a general statement that can be used to develop an alternative hypothesis, which may or may not be correct.