I agree completely with valeri. Plotting the data can help you understand what you are dealing with. Neither data set shows a convincing trend, especially the first one.
library(ggplot2)
data = data.frame(work_days = c(20,21,22,20,20,22,21,21),
sale = c(1205,2111,2452,2054,2440,1212,1211,2111))
ggplot(data, aes(work_days, sale)) + geom_point() + geom_smooth(method = "lm")

summary(lm(sale ~ work_days, data))
#>
#> Call:
#> lm(formula = sale ~ work_days, data = data)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -677.8 -604.5 218.7 339.0 645.3
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 2643.82 5614.16 0.471 0.654
#> work_days -38.05 268.75 -0.142 0.892
#>
#> Residual standard error: 593.4 on 6 degrees of freedom
#> Multiple R-squared: 0.00333, Adjusted R-squared: -0.1628
#> F-statistic: 0.02005 on 1 and 6 DF, p-value: 0.892
data = data.frame(work_days = c(20,21,22,20,20,22,21,21),
sale = c(1212,1211,2111,1205,2111,2452,2054,2440))
ggplot(data, aes(work_days, sale)) + geom_point() + geom_smooth(method = "lm")

summary(lm(sale ~ work_days, data))
#>
#> Call:
#> lm(formula = sale ~ work_days, data = data)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -686.8 -301.0 -8.6 261.3 599.7
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -6220.0 4555.9 -1.365 0.221
#> work_days 386.6 218.1 1.772 0.127
#>
#> Residual standard error: 481.5 on 6 degrees of freedom
#> Multiple R-squared: 0.3437, Adjusted R-squared: 0.2343
#> F-statistic: 3.142 on 1 and 6 DF, p-value: 0.1267
Created on 2019-09-16 by the reprex package (v0.2.1)