This is a tough one. Without understanding what you really need from the field - the amount of statistical methods in biological sciences is not as narrow as you think - it's not easy to recommend something. I doubt any other statistical programming language comes close to the amount of methods that R has (Python and Julia are close/may be better, though).

Guessing from your request for a user-friendly alternative to R, I would assume that you probably have tried R, and realised that it is a tough interface to learn, or get used to. That is fine - these things take time. (Apologies in advance if I made the wrong assumption.)

I think you can still have the best of both worlds. There is a statistical program that I stumbled upon more than a year ago - jamovi (https://www.jamovi.org/). I never got to use it, but bookmarked it. I believe it was (and still is, probably) targeted to people who want to use R, but can't get over the console-type interface. There is no doubt that it is focused on being user-friendly. It looks nothing like R, but works like R! (Think of it as a "wrapper" around R that hides the coding and replaces that with buttons and input fields.)

Jamovi is not as fully featured (for sure), but it can do the following out of the box (taken from the website):

• Descriptive statistics
• T-Tests
• ANOVA
• Repeated Measures ANOVA
• ANCOVA
• MANOVA
• Non-parametric ANOVA's
• Correlation
• Linear regression
• Binomial logistic regression
• Multinomial logistic regression
• Binomial test
• Goodness of fit
• Contingency tables
• Log-linear regression
• Reliability analysis
• Principal component analysis (PCA)
• Exploratory factor analysis (EFA)
• Confirmatory factor analysis (CFA)

The great thing with jamovi is that it is free, open, and looks like it is frequently updated. This is important! You do not want to use a free program, spend time to master it and find out that it is abandoned. It is also based on R.

A cool feature that is has is that you can point-and-click your way to analyse the data, and when you have the results, you can switch to "syntax mode" and reveal all the R code used to analyse your data. A self-learning user interface, if you will. Also, a great "insurance" to master a programming language that will most likely persist for decades... should jamovi itself lose support in the future.

Sorry for the long post.

Edit: typo

Dear Barbara

You can download the trial version of 30 days of MiniTab.

It works great.

Regards,

Anchal

JASP is open-source and has a nice UI: https://jasp-stats.org

I use it occasionally and find it useful.

Python + modules numpy, scipy, matplotlib

This is a tough one. Without understanding what you really need from the field - the amount of statistical methods in biological sciences is not as narrow as you think - it's not easy to recommend something. I doubt any other statistical programming language comes close to the amount of methods that R has (Python and Julia are close/may be better, though).

Guessing from your request for a user-friendly alternative to R, I would assume that you probably have tried R, and realised that it is a tough interface to learn, or get used to. That is fine - these things take time. (Apologies in advance if I made the wrong assumption.)

I think you can still have the best of both worlds. There is a statistical program that I stumbled upon more than a year ago - jamovi (https://www.jamovi.org/). I never got to use it, but bookmarked it. I believe it was (and still is, probably) targeted to people who want to use R, but can't get over the console-type interface. There is no doubt that it is focused on being user-friendly. It looks nothing like R, but works like R! (Think of it as a "wrapper" around R that hides the coding and replaces that with buttons and input fields.)

Jamovi is not as fully featured (for sure), but it can do the following out of the box (taken from the website):

• Descriptive statistics
• T-Tests
• ANOVA
• Repeated Measures ANOVA
• ANCOVA
• MANOVA
• Non-parametric ANOVA's
• Correlation
• Linear regression
• Binomial logistic regression
• Multinomial logistic regression
• Binomial test
• Goodness of fit
• Contingency tables
• Log-linear regression
• Reliability analysis
• Principal component analysis (PCA)
• Exploratory factor analysis (EFA)
• Confirmatory factor analysis (CFA)

The great thing with jamovi is that it is free, open, and looks like it is frequently updated. This is important! You do not want to use a free program, spend time to master it and find out that it is abandoned. It is also based on R.

A cool feature that is has is that you can point-and-click your way to analyse the data, and when you have the results, you can switch to "syntax mode" and reveal all the R code used to analyse your data. A self-learning user interface, if you will. Also, a great "insurance" to master a programming language that will most likely persist for decades... should jamovi itself lose support in the future.

Sorry for the long post.

Edit: typo

Hello Barbara,

Januar Hariato's recommendation of jamovi as a more user-friendly, front-end shell to R is a good one; other such shells are available as well, such as Deducer (http://www.deducer.org).

Here is a page that lists many free software packages; the best choice most likely depends on the kinds of analyses you intend to run: https://www.predictiveanalyticstoday.com/top-free-statistical-software/

Good luck with your work!

I don't subscribe to Hamzas view that R is essentially for the analysis of "huge or vague data"* R takes these points serious:

• documentation of the analysis workflow
• communicatability
• reproducibility
• automation (not that important here)

And I also don't see that the results of MINITAB should be more professional than those of other software (like R, in particular). How is that professionality measured?

---

*btw: "machine learning" is just another preamble for a plethora of statistical tools, including linear models [t-test, ANOVA, regression], generalized linear models [logistic regression], and also other classification and clustering tools -- nothing limited to "huge or vague data".

You are amazing!! Thank you all for your answers!!

Januar Harianto, you are absolutelly right. I have tried R and its awesome if only I had the time to learn how to use it properly and right now, I don't.

Thank you David Morse for the site with free softwares!

I'll look into Jamovi, Deducer and Jasp and hopefully one of them will help me!!

Again, thank you very much! Best regards,

B.

Hi, Barbara,

I would also recommend JASP, even when I am not too sure whether it would suffix your needs.

The main reason why I recommend it is because it provides a dual analytical potential: frequentist analyses (a.k.a., Fisher's tests of significance) and Bayesian analyses (a.k.a., Jeffreys's Bayes factors).

There is a lot of turmoil happening at the moment between both approaches, with some serious criticisms against frequentism and some serious advances by the Bayesians. Although I do not necessarily agree with the reasons for such turmoil, it may be wise to pay attention, in case the ground is really shifting. JASP allows for carrying out and reporting both types of analyses, which would benefit if the shift really happens in your field in the near or far future. (Note: You can also do Bayesian analyses in R.)

Regards.

Hi Barbara, I will recommend for JASP.

It is quite enough and significant to use the Data Analysis tool in Microsoft Excel.

Use can try WEKA: http://www.cs.waikato.ac.nz/ml/weka/

Weka is open source software issued under the GNU General Public License. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization

There is SAS on demands for academics online. You only need to register.

https://www.sas.com/en_us/software/on-demand-for-academics.html

There is a program called PSPP, as a joke on SPSS. I have not tried it myself, but it may be worth a shot?