{"id":23544,"date":"2024-05-13T10:54:54","date_gmt":"2024-05-13T10:54:54","guid":{"rendered":"http:\/\/staging.avdheshsharma.com\/the-null-hypothesis-what-researchers-often-get-wrong\/"},"modified":"2026-04-09T10:09:34","modified_gmt":"2026-04-09T04:39:34","slug":"the-null-hypothesis-what-researchers-often-get-wrong","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/the-null-hypothesis-what-researchers-often-get-wrong","title":{"rendered":"The null hypothesis: A handy guide"},"content":{"rendered":"

What is the null hypothesis?<\/a><\/p>\n

What is the alternative hypothesis?<\/a><\/p>\n

What is a p value?<\/a><\/p>\n

What is Null Hypothesis Significance Testing (NHST)?<\/a><\/p>\n

What are the drawbacks of null hypothesis significance testing?<\/a><\/p>\n

Errors in hypothesis testing<\/a><\/p>\n

What is a type 1 error?<\/a><\/p>\n

What is a type 2 error?<\/a><\/p>\n

Summing up<\/a><\/p>\n

 <\/p>\n

For biomedical researchers, it\u2019s as important to understand basic statistical concepts as it is to understand cellular pathways. Among these concepts, one of the most important is that of a null hypothesis. Often denoted as H0, the null hypothesis is the default assumption in common statistical tests like ANOVA, Pearson\u2019s correlation analysis, t-test, etc.<\/p>\n

<\/a>What is the null hypothesis?<\/h2>\n

The null hypothesis for a statistical test posits that there is no statistically significant difference, effect, or relationship between the variables under investigation. Basically, any observed differences, relationships, or effects are due to chance.<\/p>\n

<\/a>What is the alternative hypothesis?<\/h2>\n

The counter of the null hypothesis is the alternative hypothesis, often denoted as H1. The alternative hypothesis is usually the question we are interested in: that the relationship, difference, or effect observed in our data is not due to chance (i.e., it is genuine).<\/p>\n

<\/a>What is a p value?<\/h2>\n

A p-value quantifies the strength of evidence against the null hypothesis. It represents the probability of obtaining results as extreme as or more extreme than the observed data, assuming the null hypothesis is true. A lower p-value indicates stronger evidence against the null hypothesis, typically signifying statistical significance.<\/p>\n

Note that a low p-value doesn\u2019t mean that the alternative hypothesis is true. It simply means that that the results you\u2019ve got are unlikely to have occurred merely due to random chance.<\/p>\n

<\/a>What is Null Hypothesis Significance Testing (NHST)?<\/h2>\n

NHST is a method of statistical inference in which we decide whether the null hypothesis can be rejected or not.<\/p>\n

Imagine you\u2019re testing a new antihypertensive agent: the null hypothesis says it doesn’t lower blood pressure. You collect data and calculate a p-value, which measures how likely your results are if the agent had no effect. A low p-value suggests your findings are unlikely to happen by chance, supporting the idea that the agent does lower blood pressure. NHST helps researchers make confident conclusions about their discoveries, ensuring that what they find is real and not just a fluke.<\/p>\n

<\/a>What are the drawbacks of null hypothesis significance testing?<\/h2>\n

However, NHST has some widely documented shortcomings: Firstly, it focuses solely on statistical significance, ignoring effect size and practical relevance. A small p-value may indicate significance, but it doesn’t necessarily mean the effect is meaningful in real life. Another drawback of NHST is that it dichotomizes results into significant or non-significant, leading to misinterpretation and publication bias, where statistically significant findings are more likely to considered important and to be published. NHST also relies on arbitrary thresholds like 0.05, which can be misused to support weak or spurious claims. Nevertheless, despite these shortcomings, NHST is still considered a pillar of statistical inference (Nulty, 2022).<\/p>\n

<\/a>Errors in hypothesis testing<\/h2>\n

During hypothesis testing, there\u2019s a possibility of two kinds of errors:<\/p>\n