Here is a data frame.

```
> tmp_df
BRCA1_MYC2 Best_response3
1 BRCA1 0
2 BRCA1 0
3 BRCA1 0
4 BRCA1 0
5 MYC 1
6 BRCA1 1
8 MYC 1
9 BRCA1 0
10 MYC 1
```

I have performed fisher exact test and it returned a p value of 0.048.

```
> table(tmp_df$Best_response3, tmp_df$BRCA1_MYC2) %>% fisher.test()
Fisher's Exact Test for Count Data
data: .
p-value = 0.04762
alternative hypothesis: true odds ratio is not equal to 1
95 percent confidence interval:
0.6944485 Inf
sample estimates:
odds ratio
Inf
```

But when I performed logistics regression ananlysis, it returned a p value of 0.997.

```
> tmp_fit <- glm(Best_response3 ~ BRCA1_MYC2, data = tmp_df, family = 'binomial')
> summary(tmp_fit)
Call:
glm(formula = Best_response3 ~ BRCA1_MYC2, family = "binomial",
data = tmp_df)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.60386 -0.60386 -0.60386 0.00008 1.89302
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.609 1.095 -1.469 0.142
BRCA1_MYC2MYC 21.176 6208.832 0.003 0.997
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12.3653 on 8 degrees of freedom
Residual deviance: 5.4067 on 7 degrees of freedom
AIC: 9.4067
Number of Fisher Scoring iterations: 18
```

Iâ€™m wondering why so big difference exists and which p value should be considered. Thanks.