# What method is recommended after outliers removal?

I have a data of mice reaction times. In every session, there are some trials in which the mouse "decides of a break" and responds after a long time to these specific trials. I was thinking of applying outlier removal on my data. and the data does look better (I used a Matlab function which removed all data above of below 3 IQR's above and below median).

After doing that I got a histogram which is more similar to a normal distribution (below an example picture of one of my sessions).

My question is:

After applying my outlier removal, how should I analyze the remaining data?

Should I consider the Median (together with IQR as standard error mean)?

Or should I consider the mean (together with $$\frac{\sigma}{\sqrt n}$$ as standard error mean)?

Remark: I have very little knowledge in statistics, so, I there are mistakes above (for example my standard error mean definition as IQR or $$\frac{\sigma}{\sqrt n}$$ is not correct), I would be grateful if you'll let me know.

Thanks!

Edit: The purpose of my analysis is to show that under certain conditions, the response times of the mice will be faster then under other conditions.

fig 1: Data before outlier removal

fig 2: Data after outlier removal

• It depends what you want to find out? What is your (statistical) hypothesis you want to test? Depending on that, you can choose different tests/models to validate or reject your hypothesis. Without that information it is not possible to answer your question appropriate. – Maeaex1 Nov 14 '19 at 8:47
• @Maeaex1, The purpose of my analysis is to show that under certain conditions, the response times of the mice will be faster then under other conditions. Thanks – user135172 Nov 14 '19 at 9:22

based on this information I would recommend to use

or

This depends on your sample size and distribution. To test if your data set is normally distributed you can use the Jarque-Bera test.

I didn't work with Matlab yet, but I guess all the tests should be implemented in Matlab.

From that point you could evaluate the impact of the condition on response time. Visualize your data (X on Y) - Scatter plot / Heatmap (if multivariate data).

From here you could start building a model

• starting with linear regression
• going into more advanced models/approaches -- random forests, bootstrapping etc.

If you only have binary variables (condition / no condition) - you need to set up dummy variables to perform a regression model (1 / 0) . Data then could look like:

 obs  response-time   any_condition  condition_1   condition_2   condition_3
1   12.54           1              1             0             0
2   19.34           0              0             0             0
3   13.32           1              1             1             0
4   14.7            0              0             0             0


If you have multiple different "state" conditions, I would recommend to use a control variable ("any_condition") - to see if conditions (not specifying which, have an impact) on the response time.

I hope that helps.

• OK will try these. Thanks for all the tips! – user135172 Nov 18 '19 at 12:51