A financial scientist studying the effect of different strategies has serious concerns about the use of historical data for back-testing and thus prefers to test strategies using new data from the market as it becomes available. To reduce the lead time, multiple strategies were followed for 10 months and tested simultaneously using a two-way ANOVA. One set of treatments relates to risk-reduction strategies and the other focused on maximizing returns. One portfolio followed an index tracking strategy to provide a baseline against which to assess the others. Each portfolio’s Sharpe ratio [(Return_i – risk-free rate) / volatility] was calculated to provide a standard metric for assessment. Results of this test appear below:

ANOVA
Source of Variation	SS	df	MS	F	P-value	F crit
Risk-reduction strategies	146.5679	9	16.28532	0.799124	0.617328	1.905914
Return-enhancing strategies	870.2418	3	290.0806	14.23431	8.72E-09	2.629707
Interaction	726.4466	27	26.90543	1.320255	0.135013	1.516813
Within	7336.427	360	20.37897

What follow up steps are necessary to determine which (if any) of the return-enhancing strategies worked? What technique could you use to reduce the probability of committing a type 1 error when determining which (if any) offer a higher Sharpe ratio?