The choice of statistical methods is quite large. For the evaluation of biotests, you only need very specific ones.
ToxRat has all the tools you need.
And it knows which method is suitable for which data.

Hypothesis testing

Simple statistics:

• mean, median, standard deviation, coefficient of variation, confidence interval, minimum and maximum

Statistical testing

• Variance analysis (ANOVA, Kruskal-Wallis Test, chi²- and exact contingency table tests)
• Analysis of Variance plus Trend (Jonckheere-Terpstra, Cochran Armitage)
• Pretests on normal distribution  (R/S-Test, Kolmogorrov-Smirnov Test, Shapiro Wilks Test)
• Pre-tests on homogenity of variance (Cochran, Bartlett, Levene, Tarone test for extrabinomial variance)
• Tests for monotony (linear + quadratic contrasts, (Rao-Scott-) Cochran Armitage Trend Test, Jonckheere-Terpstra Trend Test)
• Pairwise (two-sample) comparisons (Student-t-Test, Welch-t-Test, Mann-Whitney-U-Test, Mediantest, Fisher Exact Binomial Test, Chi2 Fourfold Table Test)
• Multiple Comparisons (t-Test with Bonferroni-Correction, Dunnett Test, Williams Test, Welch-t-Test with Bonferroni-Correction, Step down Jonckheere Terpstra Test, Bonferroni-Median test, Wilcoxon-Mann-Withney-U-Test with Bonferroni Correction, Step down (Rao Scott-) Cochran Armitage Test, Chi² - and Fisher Exact Test with Bonferroni Correction)
• Tests for outliers (Dixon/Grubbs, Hampel outlier test)

Several data transfomations available

Point Estimation - linear regression, interpolation

Dose-Response-Curves / Find effect levels:  up to 6 user definable effect levels, 95% Confidence limits

Linear regression (metric and quantal variables):

• Functions: Probit, Logit, Weibull
• Fitting algorithms: linear / linear weighted / linear max. likelihood
• Confidence limits: Fieller´s Theorem, Normal Approximation, Bootstrap procedure
• Correction of variance for covariance of control
• Abbott Correction
• Parallel Line Assay and Potency Estimation

Interpolation methods to determine the EC50 for quantal data:
(Trimmed) Spearman Kärber, Moving Averages, Binomial estimation

Point Estimation - non linear regression

Non-linear regression

• 2-3-4 parameter Normal, Sigmoid (Bruce-Versteeg)
• 2-3-4 paramter Logistic
• 2-3-4 Parameter Weibull
• Weighting: relative, Poisson, by variability
• Optimization methods: Levenberg-Marquardt, Downhill-Simplex
• Confidence limits: Monte Carlo Simulation, Bootstrap procedure