{"id":3050,"date":"2019-02-13T14:47:01","date_gmt":"2019-02-13T14:47:01","guid":{"rendered":"https:\/\/askanacademic.com\/?p=3050"},"modified":"2019-09-19T13:26:01","modified_gmt":"2019-09-19T13:26:01","slug":"null-hypothesis-importance","status":"publish","type":"post","link":"https:\/\/askanacademic.com\/research-methodology\/null-hypothesis-importance\/","title":{"rendered":"Why Does Research Require a Null Hypothesis?"},"content":{"rendered":"\n
Question<\/h2>\n\n\n\n
What is a null hypothesis and why does research need one?<\/p>\n\n\n\n
Answer<\/h2>\n\n\n\n
Every\nresearcher is required to establish hypotheses in order to predict,\ntentatively, the outcome of the research (Leedy & Ormrod, 2016). A null\nhypothesis is \u201cthe result of chance alone\u201d, there\u2019s no patterns, differences or\nrelationships between variables (Leedy & Ormrod, 2016). Whether the outcome\nis positive or negative, the requirement of a null hypothesis in addition of\nyour alternative hypothesis means that your research (and you as the researcher\nas well) is not one-sided (Bland & Altman, 1994). In other words, you and\nthe research are open to the possibility that maybe or maybe not a difference\nbetween the variables exists and open to the possibility that the outcome of\nthe research is due to a reason (alternative hypothesis) or a chance (null\nhypothesis) (Leedy & Ormrod, 2016; Pierce, 2008 & Bland & Altman,\n1994).<\/p>\n\n\n\n
After collecting data, the hypotheses must be tested in order to reach a conclusion (Daniel & Cross, 2013). A null hypothesis is tested when the probability of the results are \u201cdue to chance alone\u201d but the data collected reasonably suggest that something (a factor, a reason or other variable) in the studied environment\/population leads to a difference\/relationship\/pattern between them (Leedy & Ormrod, 2016 & Pierce, 2008). A null hypothesis is used to draw conclusions from the collected data when the \u201cprocess of comparing data\u201d with the expected outcome (results) of chance alone (Leedy & Ormrod, 2016). When the result is because of \u201csomething other than chance\u201d, the null hypothesis is rejected and the alternative hypothesis comes to play because the data, indirectly, led us to support it (Leedy & Ormrod, 2016). The alternative hypothesis might be the one the researcher wants to be accepted, however, it \u201ccan only be accepted\u201d if after the collected data shows that the null hypothesis \u201chas been rejected\u201d (Pierce, 2008).<\/p>\n\n\n\n
References<\/h2>\n\n\n\n
Bland, J. M., & Altman, D. G. (1994). Statistics\nNotes: One and two sided tests of significance. British Medical Journal\n(BMJ), 309<\/em>, 248-248. doi:10.1136\/bmj.309.6949.248<\/p>\n\n\n\n