Null Hypothesis

{
  "id": "MLC-T-ENV-019209",
  "term": "Null Hypothesis",
  "field": "Environment",
  "definition": "1. A hypothesis stating that two variables are not related. Research attempts to disprove the null hypothesis by finding evidence of a relationship.\n\n2. The first step in testing for statistical significance in which it is assumed that the exposure is not related to disease.\n\n3. In hypothesis testing, the hypothesis that an intervention has no effect, i.e., that there is no true difference in outcomes between a treatment group and a control group. Typically, if statistical tests indicate that the >P value is at or above the specified a-level (e.g., 0.01 or 0.05), then any observed treatment effect is not statistically significant, and the null hypothesis cannot be rejected. If the P value is less than the specified a-level, then the treatment effect is statistically significant, and the null hypothesis is rejected. If a confidence interval (e.g., of 95% or 99%) includes zero treatment effect, then the null hypothesis cannot be rejected.\n\n4. The first step in testing for statistical significance in which it is assumed that the exposure is not related to disease. (CDC 2005)\n\n5. In hypothesis testing, the hypothesis that an intervention has no effect, i.e., that there is no true difference in outcomes between a treatment group and a control group. Typically, if statistical tests indicate that the >P value is at or above the specified a-level (e.g., 0.01 or 0.05), then any observed treatment effect is not statistically significant, and the null hypothesis cannot be rejected. If the P value is less than the specified a-level, then the treatment effect is statistically significant, and the null hypothesis is rejected. If a confidence interval (e.g., of 95% or 99%) includes zero treatment effect, then the null hypothesis cannot be rejected. (NLM/NICHSR 2004)\n\n6. The null hypothesis, H0, represents a theory that has been put forward, either because it is believed to be true or because it is to be used as a basis for argument, but has not been proved. For example, in a clinical trial of a new drug, the null hypothesis might be that the new drug is no better, on average, than the current drug. We would write H0: there is no difference between the two drugs on average. We give special consideration to the null hypothesis. This is due to the fact that the null hypothesis relates to the statement being tested, whereas the alternative hypothesis relates to the statement to be accepted if / when the null is rejected. The final conclusion once the test has been carried out is always given in terms of the null hypothesis. We either \"Reject H0 in favor of H1\" or \"Do not reject H0\"; we never conclude \"Reject H1,\" or even \"Accept H1.\" If we conclude \"Do not reject H0,\" this does not necessarily mean that the null hypothesis is true, it only suggests that there is not sufficient evidence against H0 in favor of H1. Rejecting the null hypothesis then, suggests that the alternative hypothesis may be true. (STEPS 1997)\n\n7. First step in testing for statistical significance in which it is assumed that the exposure is not related to disease.\n\n8. The “baseline” condition and will be rejected in favor of the alternative hypothesis only when there is overwhelming evidence the null cannot be true. [EPA 240/R-02/005]\n\n9. The “baseline” condition and will be rejected in favor of the alternative hypothesis only when there is overwhelming evidence the null cannot be true.",
  "subject": "Policy and guidance",
  "references": [
    "Program Evaluation Glossary",
    "CDC 2005",
    "NLM/NICHSR 2004",
    "Thesaurus of Terms Used in Microbial Risk Assessment",
    "CDC Epidemiology Glossary",
    "Environmental Sampling and Analytical Methods (ESAM) Program Glossary",
    "EPA 240/R-02/005"
  ],
  "url": "https://mlchart.com/terminology/environment/null-hypothesis/"
}