Fisher's Exact Test

Definition

Fisher's Exact Test is a statistical significance test used to determine if there are nonrandom associations between two categorical variables in a contingency table. It is particularly useful when sample sizes are small and when the assumptions of the chi-square test cannot be met, making it a go-to method for variant calling and analysis in bioinformatics.

5 Must Know Facts For Your Next Test

1. Fisher's Exact Test calculates the exact probability of observing the data under the null hypothesis, providing a precise p-value even with small sample sizes.
2. It is widely applied in bioinformatics for variant calling to determine if certain genetic variants are associated with specific phenotypes or conditions.
3. The test works well with 2x2 contingency tables but can be extended to larger tables, though computations become more complex.
4. Unlike the chi-square test, Fisher's Exact Test does not rely on large sample assumptions, making it more reliable for datasets with low counts.
5. Results from Fisher's Exact Test can guide further investigations into genetic associations, influencing studies in disease susceptibility and treatment responses.

Review Questions

• How does Fisher's Exact Test differ from the chi-square test when analyzing categorical data?
  • Fisher's Exact Test is specifically designed for small sample sizes and provides an exact p-value based on the hypergeometric distribution, while the chi-square test relies on asymptotic approximations and assumes larger sample sizes. This makes Fisher's test more appropriate when data counts are low or when any expected frequency in a contingency table is less than five. Therefore, when dealing with small datasets in variant calling, Fisher's Exact Test offers a more reliable assessment of association between categorical variables.
• What are the implications of using Fisher's Exact Test in variant calling for understanding genetic associations?
  • Using Fisher's Exact Test in variant calling allows researchers to evaluate whether specific genetic variants are significantly associated with particular traits or diseases. This statistical method provides precise p-values that inform the likelihood of observing such associations due to random chance. By identifying significant relationships through this test, researchers can prioritize variants for further investigation and better understand their potential roles in disease mechanisms and therapeutic responses.
• Evaluate how the use of Fisher's Exact Test impacts decision-making in bioinformatics research regarding genetic variants.
  • The utilization of Fisher's Exact Test significantly influences decision-making in bioinformatics by providing robust statistical evidence for associations between genetic variants and phenotypic outcomes. By allowing researchers to confirm or reject hypotheses about these relationships with high accuracy, it guides subsequent experimental designs and resource allocation for further studies. This has a cascading effect on understanding genetic predispositions to diseases, ultimately affecting clinical approaches and treatment strategies based on identified variants.

Related terms

Contingency Table: A data table that displays the frequency distribution of variables, often used to study the relationship between categorical variables.

P-value: The probability of obtaining results at least as extreme as those observed, under the assumption that the null hypothesis is true; used to determine statistical significance.

Chi-Square Test: A statistical test that assesses whether observed frequencies differ from expected frequencies in categorical data, typically used with larger sample sizes.